@journal{gessert-2019-nklm, Author = { R. Buchert and J. Krüger and N. Gessert and W. Lehnert and I. Apostolova and S. Klutmann and A. Schlaefer }, Title = {Deep Learning in SPECT and PET of the brain.}, Journal = {Der Nuklearmediziner.}, Year = {(2019).}, Volume = {42.}, Number = {(2),}, Pages = {118-132}, Doi = {10.1055/a-0838-8124}, Abstract = {Deep learning has led to stunning achievements in many areas in recent years, including medical image processing. After a brief discussion of the basic principles of deep learning, some selected applications of deep learning in SPECT and PET of the brain will be presented.} } @inproceedings{Bargsten-2019-curac, Author = {F. Sommer and L. Bargsten and A. Schlaefer}, Title = {IVUS-Simulation for Improving Segmentation Performance of Neural Networks via Data Augmentation.}, Journal = {CURAC 2019 Tagungsband Reutlingen.}, Year = {(2019).}, Pages = {47-51}, Month = {Sep}, Booktitle = {CURAC 2019 Tagungsband Reutlingen}, Url = {https://www.curac.org/images/advportfoliopro/images/CURAC2019/Tagungsband_Reutlingen}, Abstract = {Convolutional neural networks (CNNs) produce promising results when applied to a wide range of medical imaging tasks including the segmentation of tissue structures like the artery lumen and wall layers in intravascular ultrasound (IVUS) image data. However, large annotated datasets are needed for training to achieve sufficient performances. To increase the dataset size, data augmentation techniques like random image transformations are commonly used. In this work, we propose a new systematic approach to generate artificial IVUS image data with the ultrasound simulation software Field II in order to perform data augmentation. A simulation model was systematically tuned to a clinical data set based on the Frechet Inception Distance (FID). We show that the segmentation performance of a state\-of\-the\-art CNN with U\-Net architecture improves when pre\-trained with our synthetic IVUS data} } @article{gessert-2019-cn, Author = {J. Krueger and R. Opfer and N. Gessert and S. Schippling and A. C. Ostwaldt and C. Walker-Egger and C. Manogaran and A. Schlaefer}, Title = {Fully Automated Longitudinal Segmentation of new or Enlarging Multiple Sclerosis (MS) Lesions Using 3D Convolutional Neural Networks.}, Journal = {Clinical Neuroradiology.}, Year = {(2019).}, Volume = {29.}, Number = {(Suppl 1),}, Pages = {10}, Month = {Sep}, Url = {https://www.neurorad.de/files/content/s00062-019-00826-9.pdf} } @article{gessert-msj2019, Author = {J. Krueger and R. Opfer and N. Gessert and A. C. Ostwaldt and C. Walker-Egger and P. Manogaran and C. Wang and M. Barnett and A. Schlaefer and S. Schippling}, Title = {Fully automated lesion segmentation using heavily trained 3D convolutional neural networks are equivalent to manual expert segmentations.}, Journal = {Multiple Sclerosis Journal.}, Year = {(2019).}, Volume = {25 .}, Number = {(2_suppl),}, Pages = {844-845}, Doi = {10.1177/1352458519872904}, Url = {https://journals.sagepub.com/doi/full/10.1177/1352458519872904#articleCitationDownloadContainer} } @inproceedings{bargsten-Wendebourg-2019, Author = {L. Bargsten and M. Wendebourg and A. Schlaefer}, Title = {Data Representations for Segmentation of Vascular Structures Using Convolutional Neural Networks with U-Net Architecture.}, Year = {(2019).}, Pages = {989-992}, Month = {July}, Booktitle = {In Proc. 2019 41st IEEE Engineering in Medicine and Biology Society (EMBC'19) Berlin, Germany}, Doi = {10.1109/EMBC.2019.8857630}, Url = {https://embs.papercept.net/conferences/conferences/EMBC19/program/EMBC19_ContentListWeb_2.html}, Abstract = {Convolutional neural networks (CNNs) produce promising results when applied to a wide range of medical imaging tasks including the segmentation of tissue structures. However, segmentation is particularly challenging when the target structures are small with respect to the complete image data and exhibit substantial curvature as in the case of coronary arteries in computed tomography angiography (CTA). Therefore, we evaluated the impact of data representation of tubular structures on the segmentation performance of CNNs with U-Net architecture in terms of the resulting Dice coefficients and Hausdorff distances. For this purpose, we considered 2D and 3D input data in cross-sectional and Cartesian representations. We found that the data representation can have a substantial impact on segmentation results with Dice coefficients ranging from 60% to 82% reaching values similar to those of human expert annotations used for training and Hausdorff distances ranging from 1.38 mm to 5.90 mm. Our results indicate that a 3D cross-sectional data representation is preferable for segmentation of thin tubular structures} } @conference{bengs2019, Author = {M. Bengs and N. Gessert and A. Schlaefer}, Title = {4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification.}, Year = {(2019).}, Pages = {Accepted}, Booktitle = {Proceedings of International Conference on Medical Imaging with Deep Learning}, Url = {https://openreview.net/forum?id=HklAUVnV5V}, Abstract = {Autism spectrum disorder (ASD) is associated with behavioral and communication problems. Often, functional magnetic resonance imaging (fMRI) is used to detect and characterize brain changes related to the disorder. Recently, machine learning methods have been employed to reveal new patterns by trying to classify ASD from spatio-temporal fMRI images. Typically, these methods have either focused on temporal or spatial information processing. Instead, we propose a 4D spatio-temporal deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D convolutional neural networks and convolutional-recurrent models which outperform a previous approach with an F1-score of 0.71 compared to an F1-score of 0.65} } @inproceedings{gromniak2019, Author = {M. Gromniak and C. Brendes and A. Schlaefer}, Title = {A New Setup for Markerless Motion Compensation in TMS by Relative Head Tracking with a Small-Scale TOF Camera.}, Journal = {CURAC 2019 Tagungsband Reutlingen.}, Year = {(2019).}, Volume = {1.}, Pages = {205-210}, Month = {Sep}, Booktitle = {CURAC 2019 Tagungsband Reutlingen}, Url = {https://www.curac.org/images/advportfoliopro/images/CURAC2019/Tagungsband_Reutlingen}, Abstract = {Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be known. Often, systems such as optical digitizing scanners are used for accurate localization with a stylus. However, the approach is time-consuming as each electrode needs to be scanned manually and the scanning systems are expensive. We propose using an RGBD camera to directly track electrodes in the images using deep learning methods. Studying and evaluating deep learning methods requires large amounts of labeled data. To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup. We demonstrate that deep learning-based electrode detection is feasible with a mean absolute error of 5.69 \± 6.10 mm and that our annotation scheme provides a useful environment for studying deep learning methods for electrode detection.} } @article{schlueter19pmb, Author = {M. Schlüter and C. Fürweger and A. Schlaefer}, Title = {Optimizing Robot Motion for Robotic Ultrasound-Guided Radiation Therapy.}, Journal = {Physics in Medicine & Biology.}, Year = {(2019).}, Volume = {64.}, Number = {(19),}, Pages = {195012}, Doi = {10.1088/1361-6560/ab3bfb}, Abstract = {An important aspect of robotic radiation therapy is active compensation of target motion. Recently, ultrasound has been proposed to obtain real\-time volumetric images of abdominal organ motion. One approach to realize flexible probe placement throughout the treatment fraction is based on a robotic arm holding the ultrasound probe. However, the probe and the robot holding it may obstruct some of the beams with a potentially adverse effect on the plan quality. This can be mitigated by using a kinematically redundant robot, which allows maintaining a steady pose of the ultrasound probe while moving its elbow in order to minimize beam blocking. Ultimately, the motion of both the beam source carrying and the ultrasound probe holding robot contribute to the overall treatment time. We propose an approach to optimize the motion and coordination of both robots based on a generalized traveling salesman problem. Furthermore, we study an application of the model to a prostate treatment scenario. Because the underlying optimization problem is hard, we compare results from a state\-of\-the\-art heuristic solver and an approximation scheme with low computational effort. Our results show that integration of the robot holding the ultrasound probe is feasible with acceptable overhead in overall treatment time. For clinically realistic velocities of the robots, the overhead is less than 4\% which is a small cost for the added benefit of continuous, volumetric, and non\-ionizing tracking of organ motion over periodic X\-ray\-based tracking} } @inproceedings{schlueter19embc1, Author = {M. Schlüter and C. Fürweger and A. Schlaefer}, Title = {Optimizing Configurations for 7-DoF Robotic Ultrasound Guidance in Radiotherapy of the Prostate.}, Year = {(2019).}, Number = {(19),}, Pages = {6983-6986}, Booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, Doi = {10.1109/EMBC.2019.8857245}, Abstract = {Robotic ultrasound guidance is promising for tracking of organ motion during radiotherapy treatments, but the radio-opaque robot and probe interfere with beam delivery. The effect on treatment plan quality can be mitigated by the use of a robot arm with kinematic redundancy, such that the robot is able to elude delivered beams during treatment by changing its configuration. However, these changes require robot motion close to the patient, lead to an increased treatment time, and require coordination with the beam delivery. We propose an optimization workflow which integrates the problem of selecting suitable robot configurations into the treatment plan optimization. Starting with a large set of candidate configurations, a minimal subset is determined which provides equivalent plan quality. Our results show that, typically, six configurations are sufficient for this purpose. Furthermore, we show that optimal configurations can be reused for dose planning of subsequent patients} } @inproceedings{schlueter19spie, Author = {M. Schlüter and C. Otte and T. Saathoff and N. Gessert and A. Schlaefer}, Title = {Feasibility of a markerless tracking system based on optical coherence tomography.}, Year = {(2019).}, Pages = {accepted}, Booktitle = {SPIE Medical Imaging}, Url = {https://arxiv.org/abs/1810.12355v1} } @inproceedings{schlueter19embc2, Author = {M. Schlüter and M. M. Fuh and S. Maier and C. Otte and P. Kiani and N.-O. Hansen and R. J. Dwayne Miller and H. Schlüter and A. Schlaefer}, Title = {Towards OCT-Navigated Tissue Ablation with a Picosecond Infrared Laser (PIRL) and Mass-Spectrometric Analysis.}, Year = {(2019).}, Pages = {158-161}, Booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, Doi = {10.1109/EMBC.2019.8856808}, Abstract = {Medical lasers are commonly used in interventions to ablate tumor tissue. Recently, the picosecond infrared laser has been introduced, which greatly decreases damaging of surrounding healthy tissue. Further, its ablation plume contains intact biomolecules which can be collected and analyzed by mass spectrometry. This allows for a specific chracterization of the tissue. For a precise treatment, however, a suitable guidance is needed. Further, spatial information is required if the tissue is to be characterized at different parts in the ablated area. Therefore, we propose a system which employs optical coherence tomography as the guiding imaging modality. We describe a prototypical system which provides automatic ablation of areas defined in the image data. For this purpose, we use a calibration with a robot which drives the laser fiber and collects the arising plume. We demonstrate our system on porcine tissue samples} } @article{schlueter19ijcars, Author = {M. Schlüter and S. Gerlach and C. Fürweger and A. Schlaefer}, Title = {Analysis and Optimization of the Robot Setup for Robotic-Ultrasound-Guided Radiation Therapy.}, Journal = {International Journal of Computer Assisted Radiology and Surgery.}, Year = {(2019).}, Volume = {14.}, Number = {(8),}, Pages = {1379-1387}, Doi = {10.1007/s11548-019-02009-w}, Abstract = {Purpose Robotic ultrasound promises continuous, volumetric, and nonionizing tracking of organ motion during radiation therapy. However, placement of the robot is critical because it is radio-opaque and might severely influence the achievable dose distribution. Methods We propose two heuristic optimization strategies for automatic placement of an ultrasound robot around a patient. Considering a kinematically redundant robot arm, we compare a generic approach based on stochastic search and a more problem-specific segmentwise construction approach. The former allows for multiple elbow configurations while the latter is deterministic. Additionally, we study different objective functions guiding the search. Our evaluation is based on data for ten actual prostate cancer cases and we compare the resulting plan quality for both methods to manually chosen robot configurations previously proposed. Results The mean improvements in the treatment planning objective value with respect to the best manually selected robot position and a single elbow configuration range from 8.2 % to 32.8 % and 8.5 % to 15.5 % for segmentwise construction and stochastic search, respectively. Considering three different elbow configurations, the stochastic search results in better objective values in 80 % of the cases, with 30 % being significantly better. The optimization strategies are robust with respect to beam sampling and transducer orientation and using previous optimization results as starting point for stochastic search typically results in better solutions compared to random starting points. Conclusions We propose a robust and generic optimization scheme, which can be used to optimize the robot placement for robotic ultrasound guidance in radiation therapy. The automatic optimization further mitigates the impact of robotic ultrasound on the treatment plan quality.} } @inproceedings{schlueter19cars, Author = {M. Schlüter and S. Gerlach and C. Fürweger and A. Schlaefer}, Title = {Analysis and Optimization of the Robot Setup for Robotic-Ultrasound-Guided Radiation Therapy.}, Year = {(2019).}, Booktitle = {Presented at International Congress and Exhibition on Computer Assisted Radiology and Surgery} } @article{DBLPjournalscorrabs190106490, Author = {M.H. Laves and S. Latus and J. Bergmeier and T. Ortmaier and L. A. Kahrs and A. Schlaefer}, Title = {Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery.}, Journal = {International Journal of Computer Assisted Radiology and Surgery.}, Year = {(2019).}, Volume = {14.}, Number = {(1),}, Pages = {136-137}, Booktitle = {International Journal of Computer Assisted Radiology and Surgery}, Doi = {10.1007/s11548-019-01969-3}, Url = {http://arxiv.org/abs/1901.06490} } @conference{, Author = {N. Gessert and A. Schlaefer}, Title = {Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation.}, Year = {(2019).}, Booktitle = {International Conference on Medical Imaging with Deep Learning}, Url = {https://openreview.net/forum?id=Syg3FDjntN}, Abstract = {Typically, deep learning architectures are handcrafted for their respective learning problem. As an alternative, neural architecture search (NAS) has been proposed where the architecture\'s structure is learned in an additional optimization step. For the medical imaging domain, this approach is very promising as there are diverse problems and imaging modalities that require architecture design. However, NAS is very time-consuming and medical learning problems often involve high-dimensional data with high computational requirements. We propose an efficient approach for NAS in the context of medical, image-based deep learning problems by searching for architectures on low-dimensional data which are subsequently transferred to high-dimensional data. For OCT-based layer segmentation, we demonstrate that a search on 1D data reduces search time by 87.5% compared to a search on 2D data while the final 2D models achieve similar performance} } @article{DBLP:journals/corr/abs-1812-01464, Author = {N. Gessert and L. Wittig and D. Drömann and T. Keck and A. Schlaefer and D. B. Ellebrecht}, Title = {Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks.}, Journal = {CoRR.}, Year = {(2019).}, Volume = {abs/1812.01464.}, Url = {http://arxiv.org/abs/1812.01464} } @article{Gessert2019, Author = {N. Gessert and M. Bengs and L. Wittig and D. Drömann and T. Keck and A. Schlaefer and D. B. Ellebrecht}, Title = {Deep transfer learning methods for colon cancer classification in confocal laser microscopy images.}, Journal = {International Journal of Computer Assisted Radiology and Surgery.}, Year = {(2019).}, Month = {May}, Doi = {10.1007/s11548-019-02004-1}, Url = {https://doi.org/10.1007/s11548-019-02004-1}, Abstract = {The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback.} } @inproceedings{2019arXiv190804186G, Author = {N. Gessert and M. Gromniak and M. Bengs and L. Matthäus and A. Schlaefer}, Title = {Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels.}, Journal = {arXiv e-prints.}, Year = {(2019).}, Pages = {176-180}, Month = {Aug}, Address = {Reutlingen}, Isbn = {978-3-00-063717-9}, Booktitle = {CURAC 2019 Tagungsband}, Url = {https://arxiv.org/abs/1908.04186}, Keywords = {Computer Science - Computer Vision and Pattern Recognition}, Abstract = {Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be known. Often, systems such as optical digitizing scanners are used for accurate localization with a stylus. However, the approach is time\-consuming as each electrode needs to be scanned manually and the scanning systems are expensive. We propose using an RGBD camera to directly track electrodes in the images using deep learning methods. Studying and evaluating deep learning methods requires large amounts of labeled data. To overcome the time\-consuming data annotation, we generate a large number of ground\-truth labels using a robotic setup. We demonstrate that deep learning-based electrode detection is feasible with a mean absolute error of 5:69 \± 6:10 mm and that our annotation scheme provides a useful environment for studying deep learning methods for electrode detection} } @article{DBLP:journals/corr/abs-1810-09582, Author = {N. Gessert and M. Gromniak and M. Schlüter and A. Schlaefer}, Title = {Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation.}, Journal = {CoRR.}, Year = {(2019).}, Volume = {abs/1810.09582.}, Url = {http://arxiv.org/abs/1810.09582} } @article{8438495, Author = {N. Gessert and M. Lutz and M. Heyder and S. Latus and D. M. Leistner and Y. S. Abdelwahed and A. Schlaefer}, Title = {Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks.}, Journal = {IEEE Transactions on Medical Imaging.}, Year = {(2019).}, Volume = {38.}, Number = {(2),}, Pages = {426-434}, Month = {Feb}, Isbn = {0278-0062}, Doi = {10.1109/TMI.2018.2865659}, Keywords = {Machine learning;Image segmentation;Arteries;Image resolution;Biomedical imaging;Diseases;IVOCT;Deep Learning;Plaque Detection;Transfer Learning}, Abstract = {Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient which requires automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new dataset consisting of in-vivo patient images labeled by three trained experts. Using this dataset, we employ state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible} } @conference{, Author = {N. Gessert and M. Nielsen and M. Shaikh and R. Werner and A. Schlaefer }, Title = {Skin Lesion Classification Using Loss Balancing, Ensembles of Multi-Resolution EfficientNets and Meta Data.}, Year = {(2019).}, Address = {Shenzhen, China}, Booktitle = {ISIC Skin Lesion Classification Challenge @ MICCAI 2019}, Url = {https://challenge2019.isic-archive.com/leaderboard.html} } @inproceedings{gessert2019towards, Author = {N. Gessert and M. Schlüter and S. Latus and V. Volgger and C. Betz and A. Schlaefer}, Title = {Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods.}, Journal = {arXiv preprint arXiv:1902.03618.}, Year = {(2019).}, Pages = {accepted}, Booktitle = {International Congress and Exhibition on Computer Assisted Radiology and Surgery}, Url = {https://arxiv.org/abs/1902.03618} } @article{DBLP:journals/corr/abs-1810-09578, Author = {N. Gessert and S. Latus and Y. S. Abdelwahed and D. M. Leistner and M. Lutz and A. Schlaefer}, Title = {Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization.}, Journal = {CoRR.}, Year = {(2019).}, Volume = {abs/1810.09578.}, Url = {http://arxiv.org/abs/1810.09578} } @article{Gessert2019, Author = {N. Gessert and T. Priegnitz and T. Saathoff and S.-T. Antoni and D. Meyer and M. F. Hamann and K.-P. Jünemann and C. Otte and A. Schlaefer}, Title = {Spatio-temporal deep learning models for tip force estimation during needle insertion.}, Journal = {International Journal of Computer Assisted Radiology and Surgery.}, Year = {(2019).}, Month = {May}, Doi = {10.1007/s11548-019-02006-z}, Url = {https://doi.org/10.1007/s11548-019-02006-z}, Abstract = {Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. Hence, a number of approaches to estimate the forces at the needle have been proposed. Yet, integrating sensors into the needle tip is challenging and a careful calibration is required to obtain good force estimates.} } @article{8710336, Author = {N. Gessert and T. Sentker and F. Madesta and R. Schmitz and H. Kniep and I. Baltruschat and R. Werner and A. Schlaefer}, Title = {Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting.}, Journal = {IEEE Transactions on Biomedical Engineering.}, Year = {(2019).}, Pages = {Accepted}, Doi = {10.1109/TBME.2019.2915839}, Url = {https://arxiv.org/abs/1905.02793}, Keywords = {Lesions;Skin;Computer architecture;Medical diagnostic imaging;Image resolution;Sensitivity;Skin Lesion Classification;Deep Learning;Attention;Dermoscopy}, Abstract = {Objective: This work addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high class imbalance encountered in real-world multi-class datasets. Methods: To use high-resolution images, we propose a novel patch-based attention architecture that provides global context between small, high-resolution patches. We modify three pretrained architectures and study the performance of patch-based attention. To counter class imbalance problems, we compare oversampling, balanced batch sampling, and class-specific loss weighting. Additionally, we propose a novel diagnosis-guided loss weighting method which takes the method used for ground-truth annotation into account. Results: Our patch-based attention mechanism outperforms previous methods and improves the mean sensitivity by 7%. Class balancing significantly improves the mean sensitivity and we show that our diagnosis-guided loss weighting method improves the mean sensitivity by 3% over normal loss balancing. Conclusion: The novel patch-based attention mechanism can be integrated into pretrained architectures and provides global context between local patches while outperforming other patch-based methods. Hence, pretrained architectures can be readily used with high resolution images without downsampling. The new diagnosis-guided loss weighting method outperforms other methods and allows for effective training when facing class imbalance. Significance: The proposed methods improve automatic skin lesion classification. They can be extended to other clinical applications where high-resolution image data and class imbalance are relevant.} } @inproceedings{8956754, Author = {R. Chadda and S. Wismath and M. Hessinger and N. Schäfer and A. Schlaefer and M. Kupnik}, Title = {Needle Tip Force Sensor for Medical Applications.}, Year = {(2019).}, Pages = {1-4}, Month = {Oct}, Booktitle = {2019 IEEE SENSORS}, Doi = {10.1109/SENSORS43011.2019.8956754}, Keywords = {biomedical equipment;brachytherapy;force sensors;haptic interfaces;medical robotics;needles;strain gauges;surgery;medical applications;silicon-based semiconductor strain gauges;brachytherapy;deformation body;guiding rod;surgical needle;thinned area;strain gauge;interaction forces;measurement means;nominal needle tip force sensor;size 1.2 mm;current 1.0 mA;Force Measurement;Strain Measurement;Needle Tip Force Sensing}, Abstract = {We present a 10 N nominal needle tip force sensor based on silicon-based semiconductor strain gauges that can be used in brachytherapy or biopsies. As deformation body the guiding rod of such a surgical needle with a diameter of 1.2 mm is used. In a thinned area, near the tip of the needle, the strain gauge, as sensing element, is applied with a flexible PCB for electrical connection. Two needles are built and characterized using a testing machine. The sensors provide sensitivities of 6.1 mV/N and 6.4 mV/N for only 1 mA supply current of the bridge, respectively. The linearity and hysteresis errors are in the range of 0.6 % to 4.4 %, which allows measuring interaction forces at the needle tip. Our results prove that the force sensor will provide new measurement means or applications with haptic feedback or for robotic insertion tools} } @inproceedings{st.gerlach2019, Author = {S. Gerlach and M. Schlüter and C. Fürweger and A. Schlaefer}, Title = {Machbarkeit CNN-basierter Erzeugung von Kandidatenstrahlen für Radiochirurgie der Prostata.}, Journal = {CURAC 2019 Conference Proceedings.}, Year = {(2019).}, Pages = {128-129}, Month = {Sep}, Address = {Reutlingen}, Isbn = {978-3-00-063717-9}, Booktitle = {CURAC 2019 Tagungsband}, Url = {https://www.curac.org/images/advportfoliopro/images/CURAC2019/Tagungsband_Reutlingen}, Abstract = {Bei der Radiochirurgie wird ein Roboterarm verwendet, um Dosisabgabe aus nahezu beliebig vielen Richtungen zu ermöglichen. Allerdings ist wegen dieser Flexibilität die Behandlungsplanung eine anspruchsvolle Aufgabe. Üblicherweise wird eine Heuristik auf Grundlage randomisierter Kandidatenstrahlen verwendet, um die Anzahl der tatsächlich betrachteten Einstrahlrichtungen zu begrenzen. Im Gegensatz dazu schlagen wir die Verwendung eines Convolutional Neural Networks vor, um Kandidatenstrahlen auf Basis von Behandlungsplänen anderer Patienten zu generieren. Unsere Ergebnisse zeigen, dass dieser Ansatz nur halb so viele Kandidatenstrahlen für eine vergleichbare Planqualität benötigt.} } @inproceedings{st.gerlach2019, Author = {S. Gerlach and M. Schlüter and C. Fürweger and A. Schlaefer}, Title = {Machbarkeit CNN-basierter Erzeugung von Kandidatenstrahlen für Radiochirurgie der Prostata.}, Journal = {CURAC 2019 Conference Proceedings.}, Year = {(2019).}, Pages = {128-129}, Month = {Sep}, Address = {Reutlingen}, Isbn = {978-3-00-063717-9}, Booktitle = {CURAC 2019 Tagungsband}, Url = {https://www.curac.org/images/advportfoliopro/images/CURAC2019/Tagungsband_Reutlingen}, Abstract = {Bei der Radiochirurgie wird ein Roboterarm verwendet, um Dosisabgabe aus nahezu beliebig vielen Richtungen zu ermöglichen. Allerdings ist wegen dieser Flexibilität die Behandlungsplanung eine anspruchsvolle Aufgabe. Üblicherweise wird eine Heuristik auf Grundlage randomisierter Kandidatenstrahlen verwendet, um die Anzahl der tatsächlich betrachteten Einstrahlrichtungen zu begrenzen. Im Gegensatz dazu schlagen wir die Verwendung eines Convolutional Neural Networks vor, um Kandidatenstrahlen auf Basis von Behandlungsplänen anderer Patienten zu generieren. Unsere Ergebnisse zeigen, dass dieser Ansatz nur halb so viele Kandidatenstrahlen für eine vergleichbare Planqualität benötigt.} } @article{Latus2019a, Author = {S. Latus and F. Griese and M. Schlüter and C. Otte and M. Möddel and M. Graeser and T. Saathoff and T. Knopp and A. Schlaefer}, Title = {Bimodal intravascular volumetric imaging combining OCT and MPI.}, Journal = {Medical Physics.}, Year = {(2019).}, Volume = {46.}, Number = {(3),}, Pages = {1371-1383}, Doi = {10.1002/mp.13388}, Url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.13388}, Keywords = {biomodal imaging, intravascular optical coherence tomography (IVOCT), luminal centerline, magnetic particle imaging (MPI), vessel phantoms}, Abstract = {Purpose Intravascular optical coherence tomography (IVOCT) is a catheter-based image modality allowing for high-resolution imaging of vessels. It is based on a fast sequential acquisition of A-scans with an axial spatial resolution in the range of 5-10 µm, that is, one order of magnitude higher than in conventional methods like intravascular ultrasound or computed tomography angiography. However, position and orientation of the catheter in patient coordinates cannot be obtained from the IVOCT measurements alone. Hence, the pose of the catheter needs to be established to correctly reconstruct the three-dimensional vessel shape. Magnetic particle imaging (MPI) is a three-dimensional tomographic, tracer-based, and radiation-free image modality providing high temporal resolution with unlimited penetration depth. Volumetric MPI images are angiographic and hence suitable to complement IVOCT as a comodality. We study simultaneous bimodal IVOCT MPI imaging with the goal of estimating the IVOCT pullback path based on the 3D MPI data. Methods We present a setup to study and evaluate simultaneous IVOCT and MPI image acquisition of differently shaped vessel phantoms. First, the influence of the MPI tracer concentration on the optical properties required for IVOCT is analyzed. Second, using a concentration allowing for simultaneous imaging, IVOCT and MPI image data are acquired sequentially and simultaneously. Third, the luminal centerline is established from the MPI image volumes and used to estimate the catheter pullback trajectory for IVOCT image reconstruction. The image volumes are compared to the known shape of the phantoms. Results We were able to identify a suitable MPI tracer concentration of 2.5 mmol/L with negligible influence on the IVOCT signal. The pullback trajectory estimated from MPI agrees well with the centerline of the phantoms. Its mean absolute error ranges from 0.27 to 0.28 mm and from 0.25 mm to 0.28 mm for sequential and simultaneous measurements, respectively. Likewise, reconstructing the shape of the vessel phantoms works well with mean absolute errors for the diameter ranging from 0.11 to 0.21 mm and from 0.06 to 0.14 mm for sequential and simultaneous measurements, respectively. Conclusions Magnetic particle imaging can be used in combination with IVOCT to estimate the catheter trajectory and the vessel shape with high precision and without ionizing radiation.} } @inproceedings{inproceedings, Author = {S. Latus and M. Neidhardt and M. Lutz and N. Gessert and N. Frey and A. Schlaefer}, Title = {Quantitative Analysis of 3D Artery Volume Reconstructions Using Biplane Angiography and Intravascular OCT Imaging.}, Year = {(2019).}, Pages = {6004-6007}, Month = {07}, Booktitle = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, Doi = {10.1109/EMBC.2019.8857712}, Abstract = {Diameter and volume are frequently used parameters for cardiovascular diagnosis, e.g., to identify a stenosis of the coronary arteries. Intra-vascular OCT imaging has a high spatial resolution and promises accurate estimates of the vessel diameter. However, the actual images are reconstructed from A-scans relative to the catheter tip and imaging is subject to rotational artifacts. We study the impact of different volume reconstruction approaches on the accuracy of the vessel shape estimate. Using X-ray angiography we obtain the 3D vessel centerline and the 3D catheter trajectory, and we propose to align the A-scans using both. For comparison we consider reconstruction along a straight line and along the centerline. All methods are evaluated based on an experimental setup using a clinical angiography system and a vessel phantom with known shape. Our results ilustrate potential pitfalls in the estimation of the vessel shape, particularly when the vessel is curved. We demonstrate that the conventional reconstruction approaches may result in an overestimate of the cross-section and that the proposed approach results in a good shape agreement in general and for curver segments, with DICE coefficients of approximately 0.96 and 0.98, respectively.} } @inproceedings{antoni2019curac, Author = {S.-T. Antoni and S. Lehmann and S. Schupp and A. Schlaefer}, Title = {An Online Model Checking Approach to Soft-Tissue Detection for Rupture.}, Journal = {CURAC 2019 Tagungsband Reutlingen.}, Year = {(2019).}, Volume = {1.}, Pages = {83-88}, Month = {Sep}, Booktitle = {CURAC 2019 Tagungsband Reutlingen}, Url = {https://www.curac.org/images/advportfoliopro/images/CURAC2019/Tagungsband_Reutlingen}, Abstract = {Robotic needle insertion based on haptic feedback can be imprecise and error\-prone, especially for sudden force changes in case of ruptures. To predict rupture events early during tissue deformation, knowledge is required about the type and characteristics of the tissues involved. Several approaches to this exist and increase system complexity by including additional sensors or imaging modalities. We introduce a new approach based on formal model checking, which allows us to identify tissue by a directed search through the state space of a needle insertion model. Using force data measured at the needle shaft during cutting motion, our method identifies the most probable tissue iteratively at run\-time, based on a priori information of possible tissues. In a case study of needle insertions into gelatin phantoms with varying gelatin\-water ratios, our approach allowed 90.7% correct identifications and may thus be considered to identify tissue during robotic needle insertion} } @inproceedings{griese18iwmpi, Author = {F. Griese and S. Latus and M. Gräser and M. Möddel and M. Schlüter and C. Otte and T. Saathoff and T. Knopp and A. Schlaefer}, Title = {Stenosis analysis by synergizing MPI and intravascular OCT.}, Year = {(2018).}, Pages = {217-218}, Booktitle = {International Workshop on Magnetic Particle Imaging} } @inproceedings{mci/Padberg2018, Author = {J. Padberg and A. Schlaefer and S. Schupp}, Title = {Ein Ansatz zur nachvollziehbaren Verifikation medizinisch-cyber-physikalischer Systeme.}, Year = {(2018).}, Pages = {209-210}, Editor = {In M. Tichy and E. Bodden and M. Kuhrmann and S. Wagner and J.-P. Steghöfer (Eds.)}, Publisher = {Gesellschaft für Informatik:}, Address = {Bonn}, Booktitle = {Software Engineering und Software Management 2018}, Abstract = {Medizinische cyberphysikalische Systeme erfordern einerseits die Adaption an patientenindividuelle Parameter während einer Behandlung und andererseits den Nachweis eines sicheren Systemverhaltens. Wir schlagen vor, Nachweisbarkeit mittels Online Model-Checking und Nachvollziehbarkeit durch Anwendung von regelbasierten Transformationen zu verbinden.} } @article{Wendebourg2018, Author = {M. Wendebourg and O. Rajput and A. Schlaefer}, Title = {Detection of Simulated Clonic Seizures from Depth Camera Recordings.}, Year = {(2018).}, Volume = {6.}, Number = {(2),}, Pages = {88-94}, Booktitle = {Journal of Image and Graphics}, Doi = {10.18178/joig.6.2.88-94}, Url = {http://www.joig.org/index.php?m=content&c=index&a=show&catid=48&id=184}, Abstract = {Tonic-clonic seizures pose a serious risk of injury to those afflicted. Therefore, patients both in home-based and residential care can require constant monitoring. Technical aids may help by alerting caregivers of detected seizures. So far, the usability of several sensor systems for seizure detection has been shown. However, most of these systems require some sensors to be physically attached to the patient or are limited with respect to their accuracy or robustness. Thus, we investigated the feasibility of using depth image sequences for the detection of seizure-like periodic motion. A static camera setup was utilized to monitor a limited region of interest comparable to a patient\'s bed during the night. Data of simulated limb motion including seizure-like movement was acquired with help of a robot moving a hand phantom both uncovered and covered by a duvet, ensuring the availability of a known ground truth. Subsequently, a characteristic of the recorded images which may be used to differentiate between normal and seizure-like motion was defined. Finally, linear discriminant analysis was applied to the determined characteristic. We found that the rapid detection of seizure-like periodic motion from depth image sequences is feasible even when the moving limb is covert by a blanket} } @article{gessert2018force, Author = {N. Gessert and J. Beringhoff and C. Otte and A. Schlaefer}, Title = {Force Estimation from OCT Volumes using 3D CNNs.}, Journal = {Int J Comput Assist Radiol Surg.}, Year = {(2018).}, Volume = {13.}, Number = {(7),}, Pages = {1073–1082}, Month = {July}, Doi = {10.1007%2Fs11548-018-1777-8}, Url = {https://arxiv.org/abs/1804.10002}, Abstract = {Estimating the interaction forces of instruments and tissue is of interest, particularly to provide haptic feedback during robot-assisted minimally invasive interventions. Different approaches based on external and integrated force sensors have been proposed. These are hampered by friction, sensor size, and sterilizability. We investigate a novel approach to estimate the force vector directly from optical coherence tomography image volumes} } @conference{, Author = {N. Gessert and M. Heyder and S. Latus and D. M. Leistner and Y. S. Abdelwahed and M. Lutz and A. Schlaefer}, Title = {Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification.}, Year = {(2018).}, Month = {May}, Booktitle = {International Conference on Medical Imaging with Deep Learning}, Url = {https://arxiv.org/abs/1805.06223}, Abstract = {Deep learning methods have shown impressive results for a variety of medical problems over the last few years. However, datasets tend to be small due to time\-consuming annotation. As datasets with different patients are often very heterogeneous generalization to new patients can be difficult. This is complicated further if large differences in image acquisition can occur, which is common during intravascular optical coherence tomography for coronary plaque imaging. We address this problem with an adversarial training strategy where we force a part of a deep neural network to learn features that are independent of patient\- or acquisition\-specific characteristics. We compare our regularization method to typical data augmentation strategies and show that our approach improves performance for a small medical dataset.} } @article{gessert2018plaque, Author = {N. Gessert and M. Heyder and S. Latus and M. Lutz and A. Schlaefer}, Title = {Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning.}, Journal = {(Suppl1) International Journal of CARS'2018.}, Year = {(2018).}, Volume = {13.}, Number = {(1),}, Pages = {99-100}, Series = {13}, Booktitle = {(Suppl1) International Journal of CARS'2018}, Doi = {10.1007/s11548-018-1766-y}, Url = {https://doi.org/10.1007/s11548-018-1766-y} } @article{gessert2018pose, Author = {N. Gessert and M. Schlüter and A. Schlaefer}, Title = {A Deep Learning Approach for Pose Estimation from Volumetric OCT Data.}, Journal = {Medical Image Analysis.}, Year = {(2018).}, Volume = {46.}, Pages = {162-179}, Doi = {10.1016/j.media.2018.03.002}, Url = {https://arxiv.org/abs/1803.03852}, Abstract = {Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images\' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects.Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label\'s resolution. We achieve a mean average error of $\SI{14.89 \pm 9.3}{\micro\metre}$ and $\SI{0.096 \pm 0.072}{\degree}$ for position and orientation learning, respective.} } @conference{, Author = {N. Gessert and T. Priegnitz and T. Saathoff and S.-T. Antoni and D. Meyer and M. F. Hamann and K.-P. Jünemann and C. Otte, A. Schlaefer}, Title = {Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture - MICCAI 2018.}, Year = {(2018).}, Volume = {11073.}, Pages = {222-229, Spotlight Talk}, Booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention}, Url = {https://arxiv.org/abs/1805.11911}, Abstract = {Needle insertion is common during minimally invasive interventions such as biopsy or brachytherapy. During soft tissue needle insertion, forces acting at the needle tip cause tissue deformation and needle deflection. Accurate needle tip force measurement provides information on needle\-tissue interaction and helps detecting and compensating potential misplacement. For this purpose we introduce an image\-based needle tip force estimation method using an optical fiber imaging the deformationof an epoxy layer below the needle tip over time. For calibration andforce estimation, we introduce a novel deep learning\-based fused convolutionalGRU\-CNN model which effectively exploits the spatio\-temporaldata structure. The needle is easy to manufacture and our model achieves a mean absolute error of 1\.76 \± 1\.50 mN with a cross\-correlation coefficientof 0\.9996, clearly outperforming other methods. We test needleswith different materials to demonstrate that the approach can be adaptedfor different sensitivities and force ranges. Furthermore, we validate our approach in an ex\-vivo prostate needle insertion scenario} } @article{2018arXiv180801694G, Author = {N. Gessert and T. Sentker and F. Madesta and R. Schmitz and H. Kniep and I. Baltruschat and R. Werner and A. Schlaefer}, Title = {Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting.}, Journal = {ArXiv e-prints.}, Year = {(2018).}, Pages = {Oral. Best challenge submission with public data only. Overall 2nd placed team}, Month = {May}, Booktitle = {International Conference on Medical Imaging with Deep Learning}, Url = {https://arxiv.org/abs/1808.01694}, Keywords = {Computer Science - Computer Vision and Pattern Recognition}, Abstract = {Deep learning methods have shown impressive results for a variety of medical problems over the last few years. However, datasets tend to be small due to time\-consuming annotation. As datasets with different patients are often very heterogeneous generalization to new patients can be difficult. This is complicated further if large differences in image acquisition can occur, which is common during intravascular optical coherence tomography for coronary plaque imaging. We address this problem with an adversarial training strategy where we force a part of a deep neural network to learn features that are independent of patient\- or acquisition\-specific characteristics. We compare our regularization method to typical data augmentation strategies and show that our approach improves performance for a small medical dataset} } @inproceedings{2018arXiv180703651R, Author = {O. Rajput* and N. Gessert* and M. Gromniak and L. Matthäus and A. Schlaefer}, Title = {Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks.}, Journal = {17. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie.}, Year = {(2018).}, Pages = {138-141 *Shared First Authors }, Note = {*Shared First Authors }, Booktitle = {17. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie}, Url = {http://adsabs.harvard.edu/abs/2018arXiv180703651R}, Keywords = {Computer Science - Computer Vision and Pattern Recognition}, Abstract = {Head pose estimation and tracking is useful in variety of medical applications. With the advent of RGBD cameras like Kinect, it has become feasible to do markerless tracking by estimating the head pose directly from the point clouds. One specific medical application is robot assisted transcranial magnetic stimulation (TMS) where any patient motion is compensated with the help of a robot. For increased patient comfort, it is important to track the head without markers. In this regard, we address the head pose estimation problem using two different approaches. In the first approach, we build upon the more traditional approach of model based head tracking, where a head model is morphed according to the particular head to be tracked and the morphed model is used to track the head in the point cloud streams. In the second approach, we propose a new multi-scale convolutional neural network architecture for more accurate pose regression. Additionally, we outline a systematic data set acquisition strategy using a head phantom mounted on the robot and ground-truth labels generated using a highly accurate tracking system} } @inproceedings{latus2018towards, Author = {S. Latus and F. Griese and M. Gräser and M. Möddel and M. Schlüter and C. Otte and N. Gessert and T. Saathoff and T. Knopp and A. Schlaefer}, Title = {Towards bimodal intravascular OCT MPI volumetric imaging.}, Journal = {Proc.SPIE.}, Year = {(2018).}, Volume = {10573E.}, Pages = {10573E}, Booktitle = {Proceedings of SPIE Medical Imaging: Physics of Medical Imaging}, Organization = {International Society for Optics and Photonics}, Doi = {10.1117/12.2293497}, Url = {https://doi.org/10.1117/12.2293497}, Abstract = {Magnetic Particle Imaging (MPI) is a tracer-based tomographic non-ionizing imaging method providing fully three-dimensional spatial information at a high temporal resolution without any limitation in penetration depth. One challenge for current preclinical MPI systems is its modest spatial resolution in the range of 1 mm - 5 mm. Intravascular Optical Coherence Tomography (IVOCT) on the other hand, has a very high spatial and temporal resolution, but it does not provide an accurate 3D positioning of the IVOCT images. In this work, we will show that MPI and OCT can be combined to reconstruct an accurate IVOCT volume. A center of mass trajectory is estimated from the MPI data as a basis to reconstruct the poses of the IVOCT images. The feasibility of bimodal IVOCT and MPI imaging is demonstrated with a series of 3D printed vessel phantoms} } @conference{CyPhy2018, Author = {S. Lehmann and S.-T. Antoni and A. Schlaefer and S. Schupp }, Title = {A Quantitative Metric Temporal Logic for Execution-Time Constrained Verification.}, Year = {(2018).}, Month = {Oct 2018}, Address = {Torino, Italy}, Booktitle = {Model-Based Design of Cyber Physical Systems (CyPhy'18)} } @article{Antoni2018, Author = {S.-T. Antoni and S. Lehmann and M. Neidhardt and K. Fehrs and C. Ruprecht and F. Kording and G. Adam and S. Schupp and A. Schlaefer}, Title = {Model checking for trigger loss detection during Doppler ultrasound-guided fetal cardiovascular MRI.}, Journal = {International Journal of Computer Assisted Radiology and Surgery.}, Year = {(2018).}, Volume = {13.}, Number = {(11),}, Pages = {1755-1766}, Month = {Nov}, Doi = {10.1007/s11548-018-1832-5}, Url = {https://doi.org/10.1007/s11548-018-1832-5}, Abstract = {Ultrasound (US) is the state of the art in prenatal diagnosis to depict fetal heart diseases. Cardiovascular magnetic resonance imaging (CMRI) has been proposed as a complementary diagnostic tool. Currently, only trigger-based methods allow the temporal and spatial resolutions necessary to depict the heart over time. Of these methods, only Doppler US (DUS)-based triggering is usable with higher field strengths. DUS is sensitive to motion. This may lead to signal and, ultimately, trigger loss. If too many triggers are lost, the image acquisition is stopped, resulting in a failed imaging sequence. Moreover, losing triggers may prolong image acquisition. Hence, if no actual trigger can be found, injected triggers are added to the signal based on the trigger history.} } @article{YU2018, Author = {T. Yu, F.-A. Siebert, A. Schlaefer}, Title = {A stochastic optimization approach accounting for uncertainty in HDR brachytherapy needle Placement.}, Journal = {International Journal of Computer Assisted Radiology and Surgery CARS 2018.}, Year = {(2018).}, Volume = {13.}, Number = {(Suppl 1),}, Pages = {34-35}, Month = {Jun}, Doi = {10.1007/s11548-018-1766-y}, Url = {https://doi.org/10.1007/s11548-018-1766-y}, Abstract = {HDR brachytherapy requires the optimization of dwell times to shape the dose distribution according to the planning target volume (PTV) and organs at risk (OAR). Often, this is done after needle placement, i.e., when the needle geometry is already fixed. However, the flexibility in arranging the needles can impact the plan quality. We include the selection of the needle geometry in the inverse planning problem and study whether uncertainties due to tissue deformation and needle deflection can be handled by a novel stochastic optimization scheme. To evaluate and illustrate the approach we consider a prostate brachytherapy scenario. Particularly, we consider uncertainty in the needles tip position, e.g., due to overly conservative insertion to avoid risking bladder damage, due to errors defining the needle tip in the images, or due to the limited seed positioning repeatability of the afterloading unit.} } @article{schbmt92017, Author = {C. Hatzfeld and S. Wismath and M. Hessinger and R. Werthschützky and A. Schlaefer and M. Kupnik}, Title = {A miniaturized sensor for needle tip force measurements.}, Journal = {Biomedical Engineering / Biomedizinische Technik.}, Year = {(2017).}, Volume = {62.}, Number = {(s1),}, Pages = {s109–s115}, Month = {September}, Address = {Dresden}, Booktitle = {BMTMedPhys 2017}, Doi = {10.1515/bmt-2017-5026} } @article{Griese2017, Author = {F. Griese and T. Knopp and R. Werner and A. Schlaefer and M. Möddel }, Title = {Submillimeter-Accurate Marker Localization within Low Gradient Magnetic Particle Imaging Tomograms.}, Journal = {International Journal on Magnetic Particle Imaging.}, Year = {(2017).}, Volume = {3.}, Number = {(3),}, Pages = {1703011}, Doi = {10.15480/882.3244}, Url = {http://hdl.handle.net/11420/8442}, Abstract = {Magnetic Particle Imaging (MPI) achieves a high temporal resolution, which opens up a wide range of real-time medical applications such as device tracking and navigation. These applications usually rely on automated techniques for finding and localizing devices and fiducial markers in medical images. In this work, we show that submillimeter-accurate automatic marker localization from low gradient MPI tomograms with a spatial resolution of several millimeters is possible. Markers are initially identified within the tomograms by a thresholding-based segmentation algorithm. Subsequently, their positions are accurately determined by calculating the center of mass of the gray values inside the pre-segmented regions. A series of phantom measurements taken at full temporal resolution (46 Hz) is used to analyze statistical and systematical errors and to discuss the performance and stability of the automatic submillimeter-accurate marker localization algorithm} } @inproceedings{mgmoctnpirl, Author = {J. Dahmen and C. Otte and M. Fuh and S. Maier and M. Schlüter and S.-T. Antoni and N.-O. Hansen and R.J.D. Miller and H. Schlüter and A. Schlaefer}, Title = {Massenspektrometrische Gewebeanalyse mittels OCT-navigierter PIR-Laserablation.}, Year = {(2017).}, Pages = {112-116}, Booktitle = {16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie} } @inproceedings{kvlaino, Author = {K. V. Laino and T. Saathoff and T. R. Savarimuthu and K. Lindberg Schwaner and N. Gessert and A. Schlaefer}, Title = {Design and implementation of a wireless instrument adapter.}, Year = {(2017).}, Pages = {31-36}, Booktitle = {16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie} } @article{7849213, Author = {N. Gdaniec and M. Schlüter and M. Möddel and M. Kaul and K. Krishnan and A. Schlaefer and T. Knopp}, Title = {Detection and Compensation of Periodic Motion in Magnetic Particle Imaging.}, Journal = {IEEE Transactions on Medical Imaging.}, Year = {(2017).}, Volume = {36.}, Number = {(7),}, Pages = {1511-1521}, Isbn = {0278-0062}, Doi = {10.1109/TMI.2017.2666740}, Keywords = {Atmospheric measurements;Imaging;Particle measurements;Signal to noise ratio;Spatial resolution;Trajectory;Biomedical imaging;Motion artifacts;Motion compensation;Motion detection}, Abstract = {The temporal resolution of the tomographic imaging method magnetic particle imaging (MPI) is remarkably high. The spatial resolution is degraded for measured voltage signal with low signal-to-noise ratio, because the regularization in the image reconstruction step needs to be increased for system-matrix approaches and for deconvolution steps in x-space approaches. To improve the signal-to-noise ratio, blockwise averaging of the signal over time can be advantageous. However, since block-wise averaging decreases the temporal resolution, it prevents resolving the motion. In this paper, a framework for averaging motion-corrupted MPI raw data is proposed. The motion is considered to be periodic as it is the case for respiration and/or the heartbeat. The same state of motion is thus reached repeatedly in a time series exceeding the repetition time of the motion and can be used for averaging. As the motion process and the acquisition process are, in general, not synchronized, averaging of the captured MPI raw data corresponding to the same state of motion requires to shift the starting point of the individual frames. For high-frequency motion, a higher frame rate is potentially required. To address this issue, a binning method for using only parts of complete frames from a motion cycle is proposed that further reduces the motion artifacts in the final images. The frequency of motion is derived directly from the MPI raw data signal without the need to capture an additional navigator signal. Using a motion phantom, it is shown that the proposed method is capable of averaging experimental data with reduced motion artifacts. The methods are further validated on in-vivo data from mouse experiments to compensate the heartbeat} } @inproceedings{roctvausf, Author = {O. Ismail and O. Rajput and L. Matthäus and A. Schlaefer}, Title = {Comparison of correspondence-free and correspondence-based hand-eye calibration.}, Year = {(2017).}, Pages = {6-10}, Booktitle = {16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie} } @inproceedings{roctvausf, Author = {O. Rajput and M. Schlüter and N. Gessert and T. R. Savarimuthu and C. Otte and S.-T. Antoni and A. Schlaefer }, Title = {Robotic OCT Volume Acquisition Using a Single Fiber.}, Year = {(2017).}, Pages = {232-233}, Booktitle = {16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie} } @article{Berndt_2017, Author = {R. Berndt and R. Rusch and L. Hummitzsch and M. Lutz and K. Heß and K. Huenges and B. Panholzer and C. Otte and A. Haneya and G. Lutter and A. Schlaefer and J. Cremer and J. Groß}, Title = {Development of a new catheter prototype for laser thrombolysis under guidance of optical coherence tomography (OCT): validation of feasibility and efficacy in a preclinical model.}, Journal = {Journal of Thrombosis and Thrombolysis.}, Year = {(2017).}, Volume = {43.}, Number = {(3),}, Pages = {352-360}, Month = {jan}, Publisher = {Springer Nature:}, PMID = {28070820}, Doi = {10.1007/s11239-016-1470-0} } @inproceedings{8206481, Author = {S. Antoni and C. Otte and T. R. Savarimuthu and O. Rajput and A. Schlaefer}, Title = {Optical coherence tomography based 1D to 6D eye-in-hand calibration.}, Year = {(2017).}, Pages = {5886-5891}, Month = {Sep.}, Booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, Doi = {10.1109/IROS.2017.8206481}, Keywords = {biological tissues;biomechanics;biomedical optical imaging;calibration;optical tomography;optical coherence tomography;interferometric imaging modality;OCT signal;single optical fiber;6D robotic position system;OCT beam;intraoperative guidance;1D-to-6D eye-in-hand calibration;tissue deformation;Calibration;Robot kinematics;Three-dimensional displays;Optical imaging;Optical interferometry}, Abstract = {Optical coherence tomography (OCT) is an interferometric imaging modality with spatial resolution in the micrometer range. The OCT signal can be used to detect small structures to measure deformation or to characterize tissue. Moreover, OCT can be realized through a single optical fiber, i.e., it can be easily integrated with instruments. However, to use OCT for intra-operative guidance its spatial alignment needs to be established. Hence, we consider eye\-in\-hand calibration between the 1D OCT imaging and a 6D robotic position system. We present a method to perform pivot calibration for OCT and based on this introduce pivot + d, a new 1D to 6D eye\-in\-hand calibration. We provide detailed results on the convergence and accuracy of our method and use translational and rotational ground truth to show that our methods allow for submillimeter positioning accuracy of an OCT beam with a robot. For pivot calibration we observe a mean translational error of 0.5161 \± 0.4549 mm while pivot \+ d shows 0.3772 \± 0.2383 mm. Additionally, pivot \+ d improves rotation detection by about 8\° when compared to pivot calibration.} } @article{doi-10.1259-bjr.20160926, Author = {S. Gerlach and I. Kuhlemann and F. Ernst and C. Fürweger and A. Schlaefer }, Title = {Impact of robotic ultrasound image guidance on plan quality in SBRT of the prostate.}, Journal = {The British Journal of Radiology.}, Year = {(2017).}, Volume = {90.}, Number = {(1078),}, Pages = {20160926}, Note = {PMID: 28749165}, Doi = {10.1259/bjr.20160926}, Url = {https://doi.org/10.1259/bjr.20160926}, Abstract = {Objective:Ultrasound provides good image quality, fast volumetric imaging and is established for abdominal image guidance. Robotic transducer placement may facilitate intrafractional motion compensation in radiation therapy. We consider integration with the CyberKnife and study whether the kinematic redundancy of a seven-degrees-of-freedom robot allows for acceptable plan quality for prostate treatments.Methods:Reference treatment plans were generated for 10 prostate cancer cases previously treated with the CyberKnife. Considering transducer and prostate motion by different safety margins, 10 different robot poses, and 3 different elbow configurations, we removed all beams colliding with robot or transducer. For each combination, plans were generated using the same strict dose constraints and the objective to maximize the target coverage. Additionally, plans for the union of all unblocked beams were generated.Results:In 9 cases the planning target coverage with the ultrasound robot was within 1.1 percentage points of the reference coverage. It was 1.7 percentage points for one large prostate. For one preferable robot position, kinematic redundancy decreased the average number of blocked beam directions from 23.1 to 14.5.Conclusion:The impact of beam blocking can largely be offset by treatment planning and using a kinematically redundant robot. Plan quality can be maintained by carefully choosing the ultrasound robot position and pose. For smaller planning target volumes the difference in coverage is negligible for safety margins of up to 35?mm.Advances in knowledge:Integrating a robot for online intrafractional image guidance based on ultrasound can be realized while maintaining acceptable plan quality for prostate cancer treatments with the CyberKnife.} } @article{GeKuJaBEFS2016a, Author = {S. Gerlach and I. Kuhlemann and P. Jauer and R. Bruder and F. Ernst and C. Fürweger and A. Schlaefer}, Title = {Robotic ultrasound-guided SBRT of the prostate: feasibility with respect to plan quality.}, Journal = {Int J Comput Assist Radiol Surg. online first 1-11.}, Year = {(2017).}, Volume = {12.}, Number = {(1),}, Pages = {149-159}, Month = {Jan}, PMID = {27406743}, Url = {http://dx.doi.org/10.1007/s11548-016-1455-7}, Institution = {Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. schlaefer@tuhh.de.}, Abstract = {Advances in radiation therapy delivery systems have enabled motion compensated SBRT of the prostate. A remaining challenge is the integration of fast, non-ionizing volumetric imaging. Recently, robotic ultrasound has been proposed as an intra-fraction image modality. We study the impact of integrating a light-weight robotic arm carrying an ultrasound probe with the CyberKnife system. Particularly, we analyze the effect of different robot poses on the plan quality.A method to detect the collision of beams with the robot or the transducer was developed and integrated into our treatment planning system. A safety margin accounts for beam motion and uncertainties. Using strict dose bounds and the objective to maximize target coverage, we generated a total of 7650 treatment plans for five different prostate cases. For each case, ten different poses of the ultrasound robot and transducer were considered. The effect of different sets of beam source positions and different motion margins ranging from 5 to 50 mm was analyzed.Compared to reference plans without the ultrasound robot, the coverage typically drops for all poses. Depending on the patient, the robot pose, and the motion margin, the reduction in coverage may be up to 50 \% points. However, for all patient cases, there exist poses for which the loss in coverage was below 1 \% point for motion margins of up to 20 mm. In general, there is a positive correlation between the number of treatment beams and the coverage. While the blocking of beam directions has a negative effect on the plan quality, the results indicate that a careful choice of the ultrasound robot\'s pose and a large solid angle covered by beam starting positions can offset this effect. Identifying robot poses that yield acceptable plan quality and allow for intra-fraction ultrasound image guidance, therefore, appears feasible.} } @article{Latus2017, Author = {S. Latus and C. Otte and M. Schlüter and J. Rehra and K. Bizon and H. Schulz-Hildebrandt and T. Saathoff and G. Hüttmann and A. Schlaefer}, Title = {An Approach for Needle Based Optical Coherence Elastography Measurements.}, Journal = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017.}, Year = {(2017).}, Pages = {655-663}, Publisher = {Springer International Publishing:}, Address = {Cham}, Isbn = {978-3-319-66185-8}, Doi = {10.1007/978-3-319-66185-8_74}, Url = {https://doi.org/10.1007/978-3-319-66185-8_74}, Abstract = {While navigation and interventional guidance are typically based on image data, the images do not necessarily reflect mechanical tissue properties. Optical coherence elastography (OCE) presents a modality with high sensitivity and very high spatial and temporal resolution. However, OCE has a limited field of view of only 2–5 mm depth. We present a side-facing needle probe to image externally induced shear waves from within soft tissue. A first method of quantitative needle-based OCE is provided. Using a time of flight setup, we establish the shear wave velocity and estimate the tissue elasticity. For comparison, an external scan head is used for imaging. Results for four different phantoms indicate a good agreement between the shear wave velocities estimated from the needle probe at different depths and the scan head. The velocities ranging from 0.9–3.4 m/s agree with the expected values, illustrating that tissue elasticity estimates from within needle probes are feasible.} } @inproceedings{Latus2017a, Author = {S. Latus and M. Lutz and T. Saathoff and N. Frey and A. Schlaefer}, Title = {The quantitative effect of catheter bending on artery volume estimation for IVOCT.}, Year = {(2017).}, Address = {Rotterdam, Netherlands}, Booktitle = {Optics in Cardiology Symposium 2017}, Url = {http://www.opticsincardiology.org/program/posters/} } @inproceedings{qest2017, Author = {S. Lehmann and S.-T. Antoni and A. Schlaefer and S. Schupp }, Title = {Detection of Head Motion Artifacts based on a Statistical Online Model-Checking Approach.}, Year = {(2017).}, Pages = {accepted}, Month = {September 5-7}, Address = {Berlin, Germany}, Booktitle = {Quantitative Evaluation of Systems - 14th International Conference}, Organization = {Quantitative Evaluation of Systems - 14th International Conference}, Abstract = {Many safety-critical applications in the medical domain, including dynamic tracking systems for real-world entities and motion control systems for cyber-physical devices, need to be checked continuously to facilitate a quick reaction to system failures and environmental changes. In this paper, we describe a combined solution of online model checking and the existing statistical model checking technique, building on a model representation of possible patient behaviours. We apply our concept in a case study on head motion tracking, for which we perform online motion pattern recognition and verification to decide on the most probable pattern on each time step as a base for countermeasures} } @inproceedings{roctvausf, Author = {T. Hansen and O. Rajput and L. Mattäus and A. Schlaefer}, Title = {Evaluation of real-time trajectory planning for head motion compensation.}, Year = {(2017).}, Pages = {50-53}, Booktitle = {16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie} } @article{diercks2016features, Author = {A. Diercks and A. Schlaefer}, Title = {Features for predicting gait events using inertial measurement units.}, Journal = {Gait & Posture.}, Year = {(2016).}, Volume = {49.}, Pages = {256}, Month = {September}, Doi = {10.1016/j.gaitpost.2016.07.310}, Url = {http://dx.doi.org/10.1016/j.gaitpost.2016.07.310} } @article{AW16a, Author = {A. Patel and C. Otte and A. Schlaefer and D. Nir and S. Otte and T. Ngo and T. Loke and M. Winkler}, Title = {MP34-14 Investigating the feasibility of optical coherence tomography to identify prostate cancer - an ex-vivo study.}, Journal = {The Journal of Urology.}, Year = {(2016).}, Volume = {195.}, Number = {(4),}, Pages = {e476 - e477}, Month = {May}, Booktitle = {Presented at the Annual Meetig of American Urological Association (AUA)}, Doi = {10.1016/j.juro.2016.02.1572}, Url = {http://www.jurology.com/article/S0022-5347(16)01860-7/abstract} } @inproceedings{OtBeLaAnRS2016, Author = {C. Otte and J. Beringhoff and S. Latus and S.-T. Antoni and O. Rajput and A. Schlaefer}, Title = {Towards Force Sensing Based on Instrument-Tissue Interaction.}, Year = {(2016).}, Pages = {180-185}, Month = {September}, Address = {Baden-Baden}, Booktitle = {2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)}, Url = {https://ras.papercept.net/conferences/conferences/MFI16/program/MFI16_ContentListWeb_2.html} } @inproceedings{AOHS16a, Author = {J. Ackermann and C. Otte and G. Hüttmann and A. Schlaefer}, Title = {Methods for Needle Motion Estimation from OCT Data.}, Year = {(2016).}, Pages = {208-213}, Month = {September}, Booktitle = {15. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie} } @inproceedings{Beringhoff2016, Author = {J. Beringhoff and C. Otte and M. Schlüter and T. Saathoff and A. Schlaefer}, Title = {Kontaktlose Schätzung der Interaktionskräfte chirurgischer Instrumente..}, Year = {(2016).}, Pages = {191-196}, Booktitle = {16. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie CURAC} } @article{HBSK16a, Author = {M. Hofmann and K. Bizon and A. Schlaefer T. Knopp}, Title = {Subpixelgenaue Positionsbestimmung in Magnetic-Particle-Imaging.}, Journal = {Bildverarbeitung für die Medizin 2016, Algorithmen – Systeme – Anwendungen.}, Year = {(2016).}, Pages = {20-25}, Month = {March}, Series = {Informatik aktuell}, Booktitle = {Bildverarbeitung für die Medizin 2016 - Algorithmen - Systeme - Anwendungen}, Doi = {10.1007/978-3-662-49465-3_6}, Url = {http://dx.doi.org/10.1007/978-3-662-49465-3_6}, Abstract = {Das tomographische Bildgebungsverfahren Magnetic- Particle-Imaging (MPI) bietet eine hohe zeitliche Auflösung im unteren Millisekundenbereich. Für die Navigation von markierten Kathetern ist die Ortsauflösung jedoch zu gering. In dieser Arbeit wird gezeigt, dass eine submillimetergenaue Positionsbestimmung möglich ist, obwohl die aufgenommenen Daten eine niedrigere Auflösung aufweisen. Hierzu werden die niedrig aufgelösten MPI-Daten aufbereitet und die Position einer Probe über den Schwerpunkt der Grauwerte bestimmt. Anhand von Messdaten werden statistische Fehler und systematische Abweichungen der Methode abgeschätzt} } @conference{SchGdaScKn2016, Author = {M. Schlüter and N. Gdaniec and A. Schlaefer and T. Knopp}, Title = {Compensation of Periodic Motion for Averaging of Magnetic Particle Imaging Data.}, Year = {(2016).}, Pages = {1-2}, Month = {September}, Address = {Straßburg}, Booktitle = {IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop}, Abstract = {The temporal resolution of magnetic particle imaging (MPI) is sufficiently high to capture dynamic processes like cardiac motion. The achievable spatial resolution of MPI is closely linked to the signal-to-noise ratio of the measured voltage signal. Therefore, in practice it can be advantageous to improve the signal-to-noise ratio by block-wise averaging the signal over time. However, this will decrease the temporal resolution such that cardiac motion is not resolved anymore. In the present work, we introduce a framework for averaging MPI data that exhibit periodic motion induced by e.g. respiration and/or the heart beat. The frequency of motion is directly derived from the MPI raw data without the need for an additional navigator signal. The short time Fourier transform is used for this purpose, because each of these periodic movements will have a frequency varying over time. In order to average the captured frames corresponding to the same phase of the motion, one has to calculate virtual frames since the data acquisition and the periodic motion are not synchronized. In a phantom study it is shown that the developed method is capable of averaging experimental data without introducing any motion artifacts.} } @inproceedings{RaAnOtSaMS2016, Author = {O. Rajput and S.-T. Antoni and C. Otte and T. Saathoff and L. Matthäus and A. Schlaefer}, Title = {High Accuracy 3D Data Acquisition Using Co-Registered OCT and Kinect.}, Year = {(2016).}, Month = {September}, Booktitle = {2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems}, Url = {https://ras.papercept.net/conferences/conferences/MFI16/program/MFI16_ContentListWeb_2.html} } @article{GKEFS16a, Author = {S. Gerlach and I. Kuhlemann and F. Ernst and C. Fürweger and A. Schlaefer}, Title = {SU-G-JeP3-03: Effect of Robot Pose On Beam Blocking for Ultrasound Guided SBRT of the Prostate.}, Journal = {Medical Physics.}, Year = {(2016).}, Volume = {43.}, Number = {(6),}, Pages = {3670-3671}, Doi = {10.1118/1.4957068}, Url = {http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4957068}, Abstract = {Purpose: Ultrasound presents a fast, volumetric image modality for real-time tracking of abdominal organ motion. How-ever, ultrasound transducer placement during radiation therapy is challenging. Recently, approaches using robotic arms for intra-treatment ultrasound imaging have been proposed. Good and reliable imaging requires placing the transducer close to the PTV. We studied the effect of a seven degrees of freedom robot on the fea-sible beam directions. Methods: For five CyberKnife prostate treatment plans we established viewports for the transducer, i.e., points on the patient surface with a soft tissue view towards the PTV. Choosing a feasible transducer pose and using the kinematic redundancy of the KUKA LBR iiwa robot, we considered three robot poses. Poses 1 to 3 had the elbow point anterior, superior, and inferior, respectively. For each pose and each beam starting point, the pro-jections of robot and PTV were computed. We added a 20 mm margin accounting for organ / beam motion. The number of nodes for which the PTV was partially of fully blocked were established. Moreover, the cumula-tive overlap for each of the poses and the minimum overlap over all poses were computed. Results: The fully and partially blocked nodes ranged from 12% to 20% and 13% to 27%, respectively. Typically, pose 3 caused the fewest blocked nodes. The cumulative overlap ranged from 19% to 29%. Taking the minimum overlap, i.e., considering moving the robot?s elbow while maintaining the transducer pose, the cumulative over-lap was reduced to 16% to 18% and was 3% to 6% lower than for the best individual pose. Conclusion: Our results indicate that it is possible to identify feasible ultrasound transducer poses and to use the kinematic redundancy of a 7 DOF robot to minimize the impact of the imaging subsystem on the feasible beam directions for ultrasound guided and motion compensated SBRT. Research partially funded by DFG grants ER 817/1-1 and SCHL 1844/3-1} } @inproceedings{GKJREFS2016, Author = {S. Gerlach and I. Kuhlemann and P. Jauer and R. Bruder and F. Ernst and C. Fürweger and A. Schlaefer}, Title = {Feasibility of robotic ultrasound guided SBRT of the prostate.}, Journal = {CARS 2016, 30th International Congress and Exhibition.}, Year = {(2016).}, Address = {Heidelberg}, Booktitle = {CARS 2016, 30th International Congress and Exhibition} } @article{LaLuOtAnFS2016, Author = {S. Latus and M. Lutz and C. Otte and S.-T. Antoni and N. Frey and A. Schlaefer}, Title = {Estimation of Arterial Vasomotion Using Intravascular Optical Coherence Tomography.}, Journal = {15. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie.}, Year = {(2016).}, Month = {June } } @inproceedings{LLOSSFS2016, Author = {S. Latus and M. Lutz and C. Otte and T. Saathoff and K. Schulz and N. Frey and A. Schlaefer}, Title = {A setup for systematic evaluation and optimization of OCT imaging in the coronary arteries.}, Year = {(2016).}, Volume = {11.}, Number = {(Suppl 1),}, Pages = {175-176}, Month = {May}, Booktitle = {Proceedings, supplement of the International Journal of CARS'2016}, Doi = {10.1007/s11548-016-1412-5}, Url = {http://dx.doi.org/10.1007/s11548-016-1412-5} } @article{Prevrhal2016, Author = {S. Prevrhal and C. Spink and M. Grass and M. Bless and A. Schlaefer and H. Ittrich and M. Regier and G. Adam}, Title = {ECR 2016 Book of Abstracts - B - Scientific Sessions and Clinical Trials in Radiology.}, Journal = {Insights into Imaging.}, Year = {(2016).}, Volume = {7.}, Number = {(1),}, Pages = {162-465}, Month = {Mar}, Note = {B-1353 14:40}, Doi = {10.1007/s13244-016-0475-8}, Url = {https://doi.org/10.1007/s13244-016-0475-8} } @inproceedings{AntoSaaSch2016, Author = {S.-T. Antoni and T. Saathoff and A. Schlaefer}, Title = {On the effect of training for gesture control of a robotic microscope.}, Year = {(2016).}, Pages = {298-299}, Address = {Bern, Switzerland}, Booktitle = {CURAC 2016 - Tagungsband} } @inproceedings{AMSS2016, Author = {S.-T. Antoni and X. Ma and S. Schupp and A. Schlaefer }, Title = {Reducing false discovery rates for on-line model-checking based detection of respiratory motion artifacts.}, Year = {(2016).}, Pages = {182-186}, Month = {February}, Booktitle = {Gemeinsamer Tagungsband der Workshops der Tagung Software Engineering 2016 (SE 2016), Wien, Feb. 2016}, Abstract = {Compensating respiratory motion in radiosurgery is an important problem and can lead to a more focused dose delivered to the patient. We previously showed the negative effect of respiratory artifacts on the error of the correlation model, connecting external and internal motion, for meaningful episodes from treatments with the Accuray CyberKnife(R). We applied on-line model checking, an iterative fail safety method, to respiratory motion. In this paper we vary its prediction parameter and decrease the previously rather high false discovery rate by 30.3%. In addition, we were able to increase the number of detected meaningful episodes through adaptive parameter choice by 452%.} } @article{antoni2016online, Author = {S.-T.Antoni and J. Rinast and X. Ma and S. Schupp and A. Schlaefer }, Title = {Online model checking for monitoring surrogate-based respiratory motion tracking in radiation therapy.}, Journal = {International Journal of Computer Assisted Radiology and Surgery.}, Year = {(2016).}, Volume = {11.}, Pages = {2085-2096}, Month = {June}, Publisher = {Springer:}, Doi = {10.1007/s11548-016-1423-2}, Abstract = {Objective: Correlation between internal and external motion is critical for respiratory motion compensation in radiosurgery. Artifacts like coughing, sneezing or yawning or changes in the breathing pattern can lead to misalignment between beam and tumor and need to be detected to interrupt the treatment. We propose online model checking \(OMC\), a model-based verification approach from the field of formal methods, to verify that the breathing motion is regular and the correlation holds. We demonstrate that OMC may be more suitable for artifact detection than the prediction error. Materials and methods: We established a sinusoidal model to apply OMC to the verification of respiratory motion. The method was parameterized to detect deviations from typical breathing motion. We analyzed the performance on synthetic data and on clinical episodes showing large correlation error. In comparison, we considered the prediction error of different state-of-the-art methods based on least mean squares \(LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS\), recursive least squares \(RLSpred\) and support vector regression \(SVRpred\).Results: On synthetic data, OMC outperformed wLMS by at least 30 \% and SVRpred by at least 141 \%, detecting 70 \% of transitions. No artifacts were detected by nLMS and RLSpred. On patient data, OMC detected 23?49 \% of the episodes correctly, outperforming nLMS, wLMS, RLSpred and SVRpred by up to 544, 491, 408 and 258 \%, respectively. On selected episodes, OMC detected up to 94 \% of all events. Conclusion: OMC is able to detect changes in breathing as well as artifacts which previously would have gone undetected, outperforming prediction error-based detection. Synthetic data analysis supports the assumption that prediction is very insensitive to specific changes in breathing. We suggest using OMC as an additional safety measure ensuring reliable and fast stopping of irradiation.} } @article{AW15a, Author = {A. Tack and Y. Kobayashi and T. Gauer and A. Schlaefer and R. Werner }, Title = {Groupwise Registration for Robust Motion Field Estimation in Artifact-Affected 4D CT Images.}, Journal = {Workshop on Imaging and Computer Assistance in Radiation Therapy, MICCAI 2015.}, Year = {(2015).}, Booktitle = {Workshop on Imaging and Computer Assistance in Radiation Therapy, MICCAI 2015}, Abstract = {Precise voxel trajectory estimation in 4D CT images is a prerequisite for reliable dose accumulation during 4D treatment planning. 4D CT image data is, however, often affected by motion artifacts and applying standard pairwise registration to such data sets bears the risk of aligning anatomical structures to artifacts \– with physiologically unrealistic trajectories being the consequence. In this work, the potential of a novel non\-linear hybrid intensity\- and feature\-based groupwise registration method for robust motion field estimation in artifact\-affected 4D CT image data is investigated. The overall registration performance is evaluated on the DIR\-lab datasets; Its robustness if applied to artifact\-affected data sets is analyzed using clinically acquired data sets with and without artifacts. The proposed registration approach achieves an accuracy comparable to the state\-of\-the\-art (subvoxel accuracy), but smoother voxel trajectories compared to pairwise registration. Even more important: it maintained accuracy and trajectory smoothness in the presence of image artifacts \– in contrast to standard pairwise registration, which yields higher landmark\-based registration errors and a loss of trajectory smoothness when applied to artifact\-affected data sets} } @inproceedings{SHSSS15a, Author = {B. Hollmach and K. Schulz and S. Soltau and T. Saathoff and A. Schlaefer}, Title = {Feasibility of Robotic Ultrasound Palpation.}, Year = {(2015).}, Pages = {125-129}, Month = {September }, Address = {Bremen, Germany}, Booktitle = {14. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie, September 17-19, 2015}, Abstract = {Ultrasound elastography presents an interesting method for measuring differences in tissue stiffness inside a patient. Conventionally, palpation plays an important role in the examination of lesions. However, elastography measurements are typically displayed as images, i.e., the haptic feedback is lost. We describe a system to realize robotic ultrasound elastography and haptic feedback of differences in tissue stiffness. We also report results for initial phantom experiments which indicate that robotic ultrasound palpation is feasible} } @conference{C15a, Author = {C. Otte and A. Patel and A. Schlaefer and S. Otte and T. Loke and T. Ngo and D. Nir and M. Winkler}, Title = {Confronting the challenge of "virtual" prostate biopsy.}, Journal = {8th International Symposium on Focal Therapy and Imaging in Prostate and Kidney Cancer.}, Year = {(2015).}, Url = {http://www.erasmus.gr/microsites/1044/e-poster-catalogue}, Abstract = {Introduction The current workflow of prostate biopsy is in need of improvement. Optical Coherence Tomography (OCT) has emerged as a promising technology capable of providing a \'virtual\' tissue analysis in real time. We explored the technological feasibility of OCT in combination with computerised interpretation of optical signals and application of Machine-Learning algorithms for in-vivo tissue diagnosis. In this ?proof of concept? study we report the results of OCT imaging of fresh ex-vivo prostate tissue and signal processing, to identify cancer without the need for biopsy core processing. Methods OCT scans were obtained from 24 patients who underwent radical prostatectomy. Immediately after prostatectomy two postero-lateral tissue strips of approximately 15mm x 8mm x 6mm were prepared and coloured for orientation. Each strip was scanned twice from the capsular (outside) and the excision (inner) surface with an OCT microscope (EX1301, Vivosight Ltd.). Scan resolution was 4 x 4 x 50 microns. The EX1301 beam?s penetration depth is 2mm. A Bidirectional Dynamic Cortex Memory Network was trained and tested on randomly chosen samples of OCT A-scan data. Mean classification rate and standard deviation were calculated for 10 cycles of training/testing. Routine histopathology analysis was used as the reference standard. Results Of 46 strips, 24 were found to contain prostate cancer and 22 benign tissue on histopathological evaluation. Applying mathematical feature extraction to OCT signals acquired from the excision (inner) surface of the strips we could differentiate cancer from benign tissue. The mean classification rates archived for the test and training sets were 67.65% (0.70%) and 69.20% (1.49%), respectively. Conclusion The application of machine-learning techniques to OCT data sets, which were obtained from ex-vivo prostate tissue, provides encouraging results and highlights the potential for a ?virtual? biopsy approach. Further optimization and in-vivo application of this technique is in progress.} } @article{coxfre, Author = {D. Düwel and C. Otte and K. Schultz and T. Saathoff and A. Schlaefer}, Title = {Towards contactless optical coherence elastography with acoustic tissue excitation.}, Journal = {Current Directions in Biomedical Engineering.}, Year = {(2015).}, Volume = {1.}, Number = {(1),}, Pages = {215-219}, Month = {September}, Editor = {In O. Dössel (Eds.)}, Publisher = {De Gruyter:}, Doi = {10.1515/CDBME-2015-0054}, Abstract = {Elastography presents an interesting approach to complement image data with mechanical tissue properties. Typically, the tissue is excited by direct contact to a probe. We study contactless elastography based on optical coherence tomography (OCT) and dynamic acoustic tissue excitation with airborne sound. We illustrate the principle and an implementation using sound waves of 135 Hz to excite the tissue. The displacement is measured and results of several tests indicate the feasibility to obtain a qualitative measure of the mechanical tissue properties. The approach is interesting for optical palpation, e.g., to enhance navigation and tissue characterization in minimally invasive and robot\-assisted surgery} } @article{HSSM15a, Author = {J. Hagenah and M. Scharfschwerdt and A. Schlaefer and C. Metzner}, Title = {A machine learning approach for planning valve-sparing aortic root reconstruction.}, Journal = {Current Directions in Biomedical Engineering.}, Year = {(2015).}, Volume = {1.}, Number = {(1),}, Pages = {361-365}, Month = {September}, Note = {ISSN 2364-5504}, Doi = {10.1515/cdbme-2015-0089}, Url = {http://www.degruyter.com/dg/viewarticle.fullcontentlink:pdfeventlink/$002fj$002fcdbme.2015.1.issue-1$002fcdbme-2015-0089$002fcdbme-2015-0089.pdf?result=3&rskey=AEqZpl&t:ac=j$002fcdbme.2015.1.issue-1$002fcdbme-2015-0089$002fcdbme-2015-0089.xml}, Abstract = {Choosing the optimal prosthesis size and shape is a difficult task during surgical valve-sparing aortic root reconstruction. Hence, there is a need for surgery planning tools. Common surgery planning approaches try to model the mechanical behaviour of the aortic valve and its leaflets. However, these approaches suffer from inaccuracies due to unknown biomechanical properties and from a high computational complexity. In this paper, we present a new approach based on machine learning that avoids these problems. The valve geometry is described by geometrical features obtained from ultrasound images. We interpret the surgery planning as a learning problem, in which the features of the healthy valve are predicted from these of the dilated valve using support vector regression (SVR). Our first results indicate that a machine learning based surgery planning can be possible.} } @conference{SOHS15a, Author = {K. Schulz and C. Otte and G. Hüttmann and A. Schlaefer}, Title = {A Concept for Fail Safe Robotic Needle Insertion in Soft Tissue.}, Journal = {Gemeinsamer Tagungsband der Workshops der Tagung Software Engineering.}, Year = {(2015).}, Volume = {1337.}, Pages = {7-10}, Address = {Dresden, Germany}, Url = {http://ceur-ws.org/Vol-1337/}, Abstract = {This paper presents a concept of automatic needle placement for brachytherapy. For the success of this minimally invasive treatment, a precise and safe placement of needles inside soft tissue is fundamental. The presented concept incorporates information about the needle as well as the tissue to find a suitable needle trajectory. A patientspecific tissue model is derived from different imaging modalities and updated during the insertion. Essential for this concept is that during the robotic placement of the needle, it is continuously verified if proceeding is safe} } @conference{NRDS15a, Author = {M. Neidhardt and O. Rajput and D. Drömann and A. Schlaefer}, Title = {Mobile C-arm Deformation and its implication on Stereoscopic Localization.}, Journal = {Tagungsband der 14. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie CURAC'15.}, Year = {(2015).}, Volume = {1.}, Pages = {183-187}, Month = {September}, Address = {Bremen, Germany}, Isbn = {978-3-00-050359-7}, Booktitle = {Tagungsband der 14. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie}, Abstract = {Accurate localization in minimally invasive procedures is challenging, particularly in the presence of nonideal imaging systems. Mobile C\-arms present a widely used tool for image guidance, including stereoscopic localization of tools, e.g., during bronchoscopy. However, the localization accuracy is susceptible to non\-idealities like gravitational deformation of the C\-arm gantry. We present a simulation study quantifying the effects of the deformation on two different approaches, namely, external tracking of the gantry pose and marker based pose estimation from within the X\-ray images. A finite element model for a typical C\-arm geometry is used to estimate deformations and their effect on the localization error is determined. Results show possible offsets between the C\-arm source and detector position of up to 12 mm and a detector rotation of 1\°. Furthermore, we demonstrate that localization based on the X\-ray images is superior to external tracking of the gantry, with a target localization error of (0.67 \± 0.25) mm and (4.29 \± 0.69) mm, respectively} } @article{SSAS15a, Author = {N. Stein and T. Saathoff and S.-T. Antoni and A. Schlaefer }, Title = {Creating 3D gelatin phantoms for experimental evaluation in biomedicine.}, Journal = {Current Directions in Biomedical Engineering.}, Year = {(2015).}, Volume = {1.}, Number = {(1),}, Pages = {331-334}, Month = {September}, Note = {ISSN 2364-5504}, Doi = {10.1515/cdbme-2015-0082}, Url = {http://www.degruyter.com/dg/viewarticle.fullcontentlink:pdfeventlink/$002fj$002fcdbme.2015.1.issue-1$002fcdbme-2015-0082$002fcdbme-2015-0082.pdf?t:ac=j$002fcdbme.2015.1.issue-1$002fcdbme-2015-0082$002fcdbme-2015-0082.xml}, Keywords = {Bioprinting; tissue mimicking materials; gelatine phantom creation; 3D-printing}, Abstract = {We describe and evaluate a setup to create gelatin phantoms by robotic 3D printing. Key aspects are the large workspace, reproducibility and resolution of the created phantoms. Given its soft tissue nature, the gelatin is kept fluid during inside the system and we present parameters for additive printing of homogeneous, solid objects. The results indicate that 3D printing of gelatin can be an alternative for quickly creating larger soft tissue phantoms without the need for casting a mold.} } @inproceedings{W+15a, Author = {R. Werner and D. Schetelig and D. Säring and S.-T. Antoni and A. Dabrowski and B.-P. Diercks and R. Fliegert and A. Guse and A. Schlaefer and I. Wolf}, Title = {Analysis of initial subcellular Ca2+ signals in fluorescence microscopy data from the perspective of image and signal processing.}, Year = {(2015).}, Month = {September}, Address = {Lübeck, Germany}, Booktitle = {49th annual conference of the German Society for Biomedical Engineering (BMT'15)}, Keywords = {image processing, image analysis, fluorescence microscopy}, Abstract = {Calcium (Ca2+) signalling is essential for T cell activation, the on-switch for the adaptive immune system. It is assumed to start by localized short-lived initial Ca2+ signals \- which, yet, have not been characterized. Initial signal formation can be examined by fluorescence microscopy but requires imaging with temporal and spatial resolutions being as high as possible. This, in turn, poses challenges from the perspective of image and signal processing, which will be discussed on the basis of our current workflow [1]. Primary and Jurkat T cells were loaded with two dyes (Fluo-4, FuraRed), stimulated by anti-CD3 or anti\-CD3/CD28-coated beads, and imaged by ratiometric fluorescence microscopy (acquisition velocity up to 48fps; nominal spatial resolution 368nm). Different strategies for deconvolution and bleaching correction and their influence on quantitative measures for local Ca2+ activity were evaluated; approaches for SNR estimation and noise filtering were compared; cell shape/orientation normalization for cell population based Ca2+ signal analysis was proposed and applied. Deconvolution and bleaching correction techniques significantly influence quantitative Ca2+ dynamics measures on a single cell level. Lucy-Richardson deconvolution combined with fit\-based additive bleaching correction is assumed to be an acceptable trade\-off between computation time and image quality. Optimal noise filtering is, however, an open issue. Applied techniques range from moving averaging to more sophisticated low\-pass filtering. Cell shape/orientation normalization, population\-based Ca2+ dynamics analysis and the introduction of specific Ca2+ activity\/responsiveness measures mitigates to some degree against the influence of specific post\-processing block implementations. Fluorescence microscopy imaging of subcellular Ca2+ signals with a spatial resolution close to Abbe\'s resolution limit and rapid image acquisition is possible, but efficient processing of resulting large data sets and especially handling the trade\-off between SNR and temporal resolution remains challenging. [1] D Schetelig et al. Proc BVM 401-6 (2015); Funded by Forschungszentrum Medizintechnik Hamburg, DFG (GU360/15-1) and Landesforschungsfoerderung Hamburg.} } @inproceedings{A+15a, Author = {S.-T. Antoni and A. Dabrowski and D. Schetelig and B.-P. Diercks and R. Fliegert and R. Werner and I. Wolf and A. H. Guse and A. Schlaefer }, Title = {Segmentation of T-cells in fluorescence microscopy.}, Year = {(2015).}, Month = {August}, Address = {Milan, Italy}, Booktitle = {In Proc. IEEE Engineering in Medicine and Biology Society (EMBC'15)}, Url = {http://emb.citengine.com/event/embc-2015/paper-details?pdID=6596}, Abstract = {In the adaptive immune system Calcium (Ca2+) is acting as a fundamental on-switch. Fluorescence microscopy is used to study the underlying mechanisms. However, living cells introduce motion, hence a precise segmentation is needed to gain knowledge about motion and deformation for subsequent analysis of (sub)cellular Ca2+ activity. We extend a segmentation algorithm and evaluate its performance using a novel scheme.} } @inproceedings{AORSS15b, Author = {S.-T. Antoni and C. Otte and O. Rajput and K. Schulz and A. Schlaefer}, Title = {Hybrid Needle Localization using Co-registered Ultrasound and OCT Imaging.}, Journal = {IROS 2015 Workshop Proceedings: Navigation and Actuation of Flexible Instruments in Medical Application (NAFIMA).}, Year = {(2015).}, Pages = {24-25}, Month = {October}, Editor = {In Jessica Burgner-Kahrs and Alexander Schlaefer (Eds.)}, Address = {Hamburg, Germany}, Booktitle = {Workshop Proceedings: Navigation and Actuation of Flexible Instruments in Medical Application (NAFIMA, IROS workshop)}, Abstract = {The precise placement of needle during brachytherapy is very important for the course of the treatment. During this procedure, different organs must be spared, for instance the bladder. We present a setup to enhance the safe and precise placement of needles with the use of co-registered OCT and Ultrasound imaging as well as an articulated robot.} } @conference{AORSS15a, Author = {S.-T. Antoni and C. Otte and O. Rajput and K. Schulz and A. Schlaefer}, Title = {Combined Ultrasound and OCT Imaging for Robotic Needle Placement.}, Journal = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'15).}, Year = {(2015).}, Pages = {4737}, Month = {October}, Address = {Hamburg, Germany}, Booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'15)}, Abstract = {The precise placement of needles is a crucial requirement for many medical procedures. We present a setup to enhance the safe and precise placement of needles with respect to a certain target structure by use of co-registered Doppler-OCT and Ultrasound imaging as well as an articulated robot. External ultrasound imaging and Doppler OCT measurements from within the needle showed similar results in estimating the tissue displacement during needle insertion.} } @article{ASSS15a, Author = {S.-T. Antoni and C. Sonnenburg and T. Saathoff and A. Schlaefer}, Title = {Feasibility of interactive gesture control of a robotic microscope.}, Journal = {Current Directions in Biomedical Engineering.}, Year = {(2015).}, Volume = {1.}, Number = {(1),}, Pages = {164-167}, Month = {September}, Note = {ISSN: 2364-5504}, Doi = {10.1515/cdbme-2015-0041}, Url = {http://www.degruyter.com/dg/viewarticle.fullcontentlink:pdfeventlink/$002fj$002fcdbme.2015.1.issue-1$002fcdbme-2015-0041$002fcdbme-2015-0041.pdf?t:ac=j$002fcdbme.2015.1.issue-1$002fcdbme-2015-0041$002fcdbme-2015-0041.xml}, Keywords = {gesture control; motorized surgical microscope; gesture tracking; assistance in surgical intervention; touch-less interaction; medical robotics}, Abstract = {Robotic devices become increasingly available in the clinics. One example are motorized surgical microscopes. While there are different scenarios on how to use the devices for autonomous tasks, simple and reliable interaction with the device is a key for acceptance by surgeons. We study, how gesture tracking can be integrated within the setup of a robotic microscope. In our setup, a Leap Motion Controller is used to track hand motion and adjust the field of view accordingly. We demonstrate with a survey that moving the field of view over a specified course is possible even for untrained subjects. Our results indicate that touch-less interaction with robots carrying small, near field gesture sensors is feasible and can be of use in clinical scenarios, where robotic devices are used in direct proximity of patient and physicians.} } @inproceedings{ARSS15a, Author = {S.-T. Antoni and J. Rinast and S. Schupp and A. Schlaefer }, Title = {Comparing Model-free Motion Prediction and On-line Model Checking for Respiratory Motion Management.}, Journal = {Software Engineering (Workshops) 2015.}, Year = {(2015).}, Pages = {15-18}, Address = {Dresden, Germany}, Booktitle = {Gemeinsamer Tagungsband der Workshops der Tagung Software Engineering}, Url = {http://ceur-ws.org/Vol-1337/paper4.pdf}, Abstract = {Compensating for respiratory motion is a key challenge for stereotactic body radiation therapy. To overcome latencies in the systems, prediction of future motion is necessary. This is related to the assumption of a stable correlation between external and internal motion. We present a new application for on-line model checking to introduce fail-safety to respiratory motion prediction and show its relevance by comparing to the widely used nLMS predictor. We demonstrate that the regularity of the external motion can be modeled and tested using OMC and deviations from regular respiratory motion can be detected.} } @inproceedings{ARSS15b, Author = {S.-T. Antoni and J. Rinast and S. Schupp and A. Schlaefer}, Title = {Evaluation des Einflusses von Artefakten auf den Korrelationsfehler in der bewegungskompensierten Radiochirurgie.}, Journal = {CURAC 2015.}, Year = {(2015).}, Pages = {133-138}, Address = {Bremen, Germany}, Booktitle = {Tagungsband der 14. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie}, Abstract = {Die Kompensation von Atembewegung in der Radiochirurgie ist ein wichtiges Problem. Durch Atembewegungsausgleich kann die Strahlung besser fokussiert und so die Gesamtdosis für den Patienten verringert werden. Bisher ist es nicht möglich, die Position des Tumors während der Behandlung kontinuierlich zu bestimmen ohne den Patienten einer deutlich erhöhten Strahlungsdosis auszusetzen. Stattdessen wird die Atmung extern gemessen. Eine wichtige Voraussetzung für die Kompensation der Atembewegung ist daher die Bestimmung eines Korrelationsmodells, dass aus den externen Atmungsbewegungsdaten die Position des Tumors schätzt. Bisher ist nur wenig darüber bekannt, inwiefern Irregularitäten in der Atmung den Fehler des Korrelationsmodells beeinflussen. Wir wenden On-line Model Checking, ein iteratives Verfahren aus der Ausfallsicherheit, auf 194 mit dem CyberKnife(R) System aufgenommene Behandlungsdatensätze an, um Artefakte in diesen zu erkennen. Für physiologisch sinnvoll definierte Episoden können wir zeigen, dass Artefakte den Korrelationsfehler negativ beeinflussen können.} } @inproceedings{APDS15a, Author = {S.-T. Antoni and R. Plagge and R. Dürichen and A. Schlaefer }, Title = {Detecting Respiratory Artifacts from Video Data.}, Journal = {Informatik aktuell.}, Year = {(2015).}, Pages = {227-232}, Month = {February}, Editor = {In Handels, Heinz and Deserno, Thomas Martin and Meinzer, Hans-Peter and Tolxdorff, Thomas (Eds.)}, Publisher = {Springer Berlin Heidelberg:}, Series = {Informatik aktuell}, Isbn = {978-3-662-46223-2}, Booktitle = {Bildverarbeitung für die Medizin 2015 Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck}, chapter = {Bildverarbeitung für die Medizin 2015}, Doi = {10.1007/978-3-662-46224-9_40}, Url = {http://link.springer.com/chapter/10.1007%2F978-3-662-46224-9_40}, Abstract = {Detecting artifacts in signals is an important problem in a wide number of research areas. In robotic radiotherapy motion prediction is used to overcome latencies in the setup, with robustness effected by the occurrence of artifacts. For motion prediction the detection and especially the definition of artifacts can be challenging. We study the detection of artifacts like, e.g., coughing, sneezing or yawning. Manual detection can be time consuming. To assist manual annotation, we introduce a method based on kernel density estimation to detect intervals of artifacts on video data. We evaluate our method on a small set of test subjects. With 86 intervals of artifacts found by our method we are able to identify all 70 intervals derived from manual detection. Our results indicate a more exact choice of intervals and the identification of subtle artifacts like swallowing, that where missed in the manual detection.} } @article{B14a, Author = {B. Wang and A. Schlaefer and Z. Zhang}, Title = {An optical approach to validate ultrasound surface segmentation of the heart.}, Journal = {SPIE Proceedings.}, Year = {(2014).}, Volume = {9230.}, Note = {Twelfth International Conference on Photonics and Imaging in Biology and Medicine}, Doi = {10.1117/12.2069023}, Abstract = {The patient specific geometry of the heart is of interest for a number of diagnostic methods, e.g., when modeling the inverse electrocardiography (ECG) problem. One approach to get images of the heart is three-dimensional ultrasound. However, segmentation of the surface is complicated and segmentation methods are typically validated against manually drawn contours. This requires considerable expert knowledge. Hence, we have developed a setup that allows studying the accuracy of image segmentation from cardiac ultrasound. Using an optical tracking system, we have measured the three-dimensional surface of an isolated porcine heart. We studied whether the actual geometry can be reconstructed from both optical and ultrasound images. We illustrate the use of our approach in quantifying the segmentation result for a three-dimensional region-based active contour algorithm. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only} } @article{OOWHKD14a, Author = {C. Otte and S. Otte and L. Wittig and G. Hüttmann and C. Kugler and D. Drömann and A. Zell and A. Schlaefer}, Title = {Investigating Recurrent Neural Networks for OCT A-scan Based Tissue Analysis.}, Journal = {Methods Inf Med.}, Year = {(2014).}, Volume = {53.}, Number = {(4),}, Pages = {245-249}, Month = {Jul}, PMID = {24992968}, Doi = {10.3414/ME13-01-0135}, Institution = {C. Otte, TU Hamburg-Harburg, Schwarzenbergstr. 95 E, room 3.088, 21073 Hamburg, Germany.}, Abstract = {Objectives: Optical Coherence Tomography (OCT) has been proposed as a high resolution image modality to guide transbronchial biopsies. In this study we address the question, whether individual A-scans obtained in needle direction can contribute to the identification of pulmonary nodules. Methods: OCT A-scans from freshly resected human lung tissue specimen were recorded through a customized needle with an embedded optical fiber. Bidirectional Long Short Term Memory networks (BLSTMs) were trained on randomly distributed training and test sets of the acquired A-scans. Patient specific training and different pre-processing steps were evaluated. Results: Classification rates from 67.5\% up to 76\% were archived for different training scenarios. Sensitivity and specificity were highest for a patient specific training with 0.87 and 0.85. Low pass filtering decreased the accuracy from 73.2\% on a reference distribution to 62.2\% for higher cutoff frequencies and to 56\% for lower cutoff frequencies. Conclusion: The results indicate that a grey value based classification is feasible and may provide additional information for diagnosis and navigation. Furthermore, the experiments show patient specific signal properties and indicate that the lower and upper parts of the frequency spectrum contribute to the classification.} } @article{LS14a, Author = {L. Wittig and C. Otte and S. Otte and G. Hüttmann and D. Drömann and A. Schlaefer}, Title = {Tissue analysis of solitary pulmonary nodules using OCT A-Scan imaging needle probe.}, Journal = {The European respiratory journal.}, Year = {(2014).}, Volume = {44.}, Number = {(58),}, Pages = {4979}, Note = {The European respiratory journal [0903-1936] Wittig, L J.:2014 Bd.:44 iss:Suppl 58 S.:P4979}, Url = {http://erj.ersjournals.com/content/44/Suppl_58/P4979.short#} } @article{LDS14a, Author = {N. Lessmann and D. Drömann and A. Schlaefer,}, Title = {Feasibility of respiratory motion-compensated stereoscopic X-ray tracking for bronchoscopy.}, Journal = {Int J Comput Assist Radiol Surg.}, Year = {(2014).}, Volume = {9.}, Number = {(2),}, Pages = {199-209}, PMID = {23888315}, Doi = {10.1007/s11548-013-0920-9} } @article{SBKS14a, Author = {O. Shahin and A. Beširevic and M. Kleemann and A. Schlaefer}, Title = {Ultrasound-based tumor movement compensation during navigated laparoscopic liver interventions.}, Journal = {Surg Endosc.}, Year = {(2014).}, Volume = {28.}, Number = {(5),}, Pages = {1734-1741}, Doi = {10.1007/s00464-013-3374-9} } @article{DWESS14a, Author = {R. Dürichen and T. Wissel and F. Ernst and A. Schlaefer and A. Schweikard}, Title = {Multivariate respiratory motion prediction.}, Journal = {Phys Med Biol.}, Year = {(2014).}, Volume = {59.}, Number = {(20),}, Pages = {6043-6060}, Doi = {10.1088/0031-9155/59/20/6043} } @article{T14b, Author = {T. Viulet and O. Blanck and A. Schlaefer}, Title = {SU-E-T-42: Analysis of Spatial Trade-Offs for a Spinal SRS Case.}, Journal = {Medical physics.}, Year = {(2014).}, Volume = {41.}, Pages = {231}, Note = {Medical physics [0094-2405] Viulet, T J.:2014 Bd.:41 iss:6 S.:231 -231}, Doi = {10.1118/1.4888372} } @article{T14a, Author = {T. Viulet and O. Blanck and A. Schlaefer}, Title = {SU-E-T-258: Parallel Optimization of Beam Configurations for CyberKnife Treatments.}, Journal = {Medical physics.}, Year = {(2014).}, Volume = {41.}, Pages = {283}, Note = {Medical physics [0094-2405] Viulet, T J.:2014 Bd.:41 iss:6 S.:283 -283}, Doi = {10.1118/1.4888589}, Abstract = {Purpose: The CyberKnife delivers a large number of beams originating at different non-planar positions and with different orientation. We study how much the quality of treatment plans depends on the beams considered during plan optimization. Particularly, we evaluate a new approach to search for optimal treatment plans in parallel by running optimization steps concurrently. Methods: So far, no deterministic, complete and efficient method to select the optimal beam configuration for robotic SRS/SBRT is known. Considering a large candidate beam set increases the likelihood to achieve a good plan, but the optimization problem becomes large and impractical to solve. We have implemented an approach that parallelizes the search by solving multiple linear programming problems concurrently while iteratively resampling zero weighted beams. Each optimization problem contains the same set of constraints but different variables representing candidate beams. The search is synchronized by sharing the resulting basis variables among the parallel optimizations. We demonstrate the utility of the approach based on an actual spinal case with the objective to improve the coverage. Results: The objective function is falling and reaches a value of 5000 after 49, 31, 25 and 15 iterations for 1, 2, 4, and 8 parallel processes. This corresponds to approximately 97\% coverage in 77\%, 59\%, and 36\% of the mean number of iterations with one process for 2, 4, and 8 parallel processes, respectively. Overall, coverage increases from approximately 91.5\% to approximately 98.5\%. Conclusion: While on our current computer with uniform memory access the reduced number of iterations does not translate into a similar speedup, the approach illustrates how to effectively parallelize the search for the optimal beam configuration. The experimental results also indicate that for complex geometries the beam selection is critical for further plan optimization.} } @article{SVMF13a, Author = {A. Schlaefer and T. Viulet and A. Muacevic and C. Fürweger}, Title = {Multicriteria optimization of the spatial dose distribution.}, Journal = {Med Phys.}, Year = {(2013).}, Volume = {40.}, Number = {(12),}, Pages = {121720}, Month = {Dec}, PMID = {24320506}, Doi = {10.1118/1.4828840}, Institution = {Medical Robotics Group, Universita?t zu Lu?beck, Lu?beck 23562, Germany and Institute of Medical Technology, Hamburg University of Technology, Hamburg 21073, Germany.}, Abstract = {Treatment planning for radiation therapy involves trade-offs with respect to different clinical goals. Typically, the dose distribution is evaluated based on few statistics and dose-volume histograms. Particularly for stereotactic treatments, the spatial dose distribution represents further criteria, e.g., when considering the gradient between subregions of volumes of interest. The authors have studied how to consider the spatial dose distribution using a multicriteria optimization approach.The authors have extended a stepwise multicriteria optimization approach to include criteria with respect to the local dose distribution. Based on a three-dimensional visualization of the dose the authors use a software tool allowing interaction with the dose distribution to map objectives with respect to its shape to a constrained optimization problem. Similarly, conflicting criteria are highlighted and the planner decides if and where to relax the shape of the dose distribution.To demonstrate the potential of spatial multicriteria optimization, the tool was applied to a prostate and meningioma case. For the prostate case, local sparing of the rectal wall and shaping of a boost volume are achieved through local relaxations and while maintaining the remaining dose distribution. For the meningioma, target coverage is improved by compromising low dose conformality toward noncritical structures. A comparison of dose-volume histograms illustrates the importance of spatial information for achieving the trade-offs.The results show that it is possible to consider the location of conflicting criteria during treatment planning. Particularly, it is possible to conserve already achieved goals with respect to the dose distribution, to visualize potential trade-offs, and to relax constraints locally. Hence, the proposed approach facilitates a systematic exploration of the optimal shape of the dose distribution.} } @inproceedings{SEWZS13a, Author = {B. Stender and F. Ernst and B. Wang and Z.X. Zhang and A. Schlaefer}, Title = {Motion compensation of optical mapping signals from isolated beating rat hearts.}, Journal = {Applications of Digital Image Processing XXXVI.}, Year = {(2013).}, Volume = {8856.}, Pages = {88561C-88561C}, Booktitle = {SPIE Optical Engineering+ Applications}, Organization = {International Society for Optics and Photonics}, Abstract = {Optical mapping is a well established technique for recording monophasic action potential traces on the epicardial surface of isolated hearts. This measuring technique offers a high spatial resolution but it is sensitive towards myocardial motion. Motion artifacts occur because the mapping between a certain tissue portion sending out fluorescent light and a pixel of the photo detector changes over time. So far this problem has been addressed by suppressing the motion or ratiometric imaging. We developed a different approach to compensate the motion artifacts based on image registration. We could demonstrate how an image deformation field temporally changing with the heart motion could be determined. Using these deformation field time series for image transformation motion signals could be generated for each image pixel which were then successfully applied to remove baseline shift and compensate motion artifacts potentially leading to errors within maps of the first arrival time. The investigation was based on five different rat hearts stained with Di\-4\-ANEPPS} } @conference{B13a, Author = {B. Stender and O. Blanck and B. Wang and A. Schlaefer}, Title = {An active shape model for porcine whole heart segmentation from multi-slice computed tomography images.}, Journal = {Computer Assisted Radiology and Surgery (CARS'2013).}, Year = {(2013).} } @article{WSLZS13a, Author = {B. Wang and B. Stender and T. Long and Z. Zhang and A. Schlaefer}, Title = {An approach to validate ultrasound surface segmentation of the heart.}, Journal = {Biomedical Engineering / Biomedizinische Technik.}, Year = {(2013).}, Volume = {58.}, Number = {(Suppl. 1),}, Doi = {10.1515/bmt-2013-4283} } @conference{C13a, Author = {C. Otte and S. Otte and L. Wittig and G. Hüttmann and D. Drömann and A. Schlaefer}, Title = {Identifizierung von Tumorgewebe in der Lunge mittels optischer Kohärenztomographie.}, Year = {(2013).}, Month = {Sep}, Booktitle = {58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)}, Doi = {10.3205/13gmds069}, Url = {http://www.egms.de/static/en/meetings/gmds2013/13gmds069.shtml} } @article{EDSS13a, Author = {F. Ernst and R. Dürichen and A. Schlaefer and A. Schweikard}, Title = {Evaluating and comparing algorithms for respiratory motion prediction.}, Journal = {Phys Med Biol.}, Year = {(2013).}, Volume = {58.}, Number = {(11),}, Pages = {3911-3929}, Doi = {10.1088/0031-9155/58/11/3911} } @article{HS13a, Author = {F. Hartmann and A. Schlaefer,}, Title = {Feasibility of touch-less control of operating room lights.}, Journal = {Int J Comput Assist Radiol Surg.}, Year = {(2013).}, Volume = {8.}, Number = {(2),}, Pages = {259-268}, PMID = {22806717}, Doi = {10.1007/s11548-012-0778-2}, Abstract = {PURPOSE\: Today\'s highly technical operating rooms lead to fairly complex surgical workflows where the surgeon has to interact with a number of devices, including the operating room light. Hence, ideally, the surgeon could direct the light without major disruption of his work. We studied whether a gesture tracking\-based control of an automated operating room light is feasible. METHODS\: So far, there has been little research on control approaches for operating lights. We have implemented an exemplary setup to mimic an automated light controlled by a gesture tracking system. The setup includes a articulated arm to position the light source and an off\-the\-shelf RGBD camera to detect the user interaction. We assessed the tracking performance using a robot\-mounted hand phantom and ran a number of tests with 18 volunteers to evaluate the potential of touch\-less light control. RESULTS\: All test persons were comfortable with using the gesture\-based system and quickly learned how to move a light spot on flat surface. The hand tracking error is direction\-dependent and in the range of several centimeters, with a standard deviation of less than 1 mm and up to 3.5 mm orthogonal and parallel to the finger orientation, respectively. However, the subjects had no problems following even more complex paths with a width of less than 10 cm. The average speed was 0.15 m\/s, and even initially slow subjects improved over time. Gestures to initiate control can be performed in approximately 2 s. Two\-thirds of the subjects considered gesture control to be simple, and a majority considered it to be rather efficient. CONCLUSIONS\: Implementation of an automated operating room light and touch\-less control using an RGBD camera for gesture tracking is feasible. The remaining tracking error does not affect smooth control, and the use of the system is intuitive even for inexperienced users.} } @article{HSSOFS13b, Author = {J. Hagenah and M. Scharfschwerdt and B. Stender and S. Ott and R. Friedl and H.H. Sievers and A. Schlaefer}, Title = {A setup for ultrasound based assessment of the aortic root geometry.}, Journal = {Biomedical Engineering/Biomedizinische Technik.}, Year = {(2013).}, Volume = {58.}, Number = {(Suppl. 1),}, PMID = {24043084}, Doi = {10.1515/bmt-2013-4379} } @article{RTSS13a, Author = {L. Richter and P. Trillenberg and A. Schweikard and A. Schlaefer}, Title = {Stimulus intensity for hand held and robotic transcranial magnetic stimulation.}, Journal = {Brain Stimul.}, Year = {(2013).}, Volume = {6.}, Number = {(3),}, Pages = {315-321}, Doi = {10.1016/j.brs.2012.06.002}, Abstract = {Background: Transcranial Magnetic Stimulation (TMS) is based on a changing magnetic field inducing an electric field in the brain. Conventionally, the TMS coil is mounted to a static holder and the subject is asked to avoid head motion. Additionally, head resting frames have been used. In contrast, our robotized TMS system employs active motion compensation (MC) to maintain the correct coil position. Objective/hypothesis: We study the effect of patient motion on TMS. In particular, we compare different coil positioning techniques with respect to the induced electric field. Methods: We recorded head motion for six subjects in three scenarios: (a) avoiding head motion, (b) using a head rest, and (c) moving the head freely. Subsequently, the motion traces were replayed using a second robot to move a sensor to measure the electric field in the target region. These head movements were combined with 2 types of coil positioning: (1) using a coil holder and (2) using robotized TMS with MC. Results: After 30 min the induced electric field was reduced by 32.0% and 19.7% for scenarios (1a) and (1b), respectively. For scenarios (2a)\–(2c) it was reduced by only 4.9%, 1.4% and 2.0%, respectively, which is a significant improvement (P < 0.05). Furthermore, the orientation of the induced field changed by 5.5°, 7.6°, 0.4°, 0.2°, 0.2° for scenarios (1a)\–(2c).} } @conference{O13a, Author = {O. Shahin and M. Kleemann and A. Schlaefer}, Title = {Monitoring tumor location in navigated laparoscopic liver surgery.}, Journal = {Computer Assisted Radiology and Surgery (CARS).}, Year = {(2013).}, Booktitle = {Computer Assisted Radiology and Surgery (CARS)} } @conference{RS13a, Author = {R. Dürichen and O. Blanck and J. Dunst and G. Hildebrandt and A. Schlaefer and A. Schweikard}, Title = {Atemphasenabhängige Prädiktionsfehler in der extrakraniellen stereotaktischen Strahlentherapie.}, Year = {(2013).}, Booktitle = {Deutschen Gesellschaft für Radioonkologie (DEGRO)}, Abstract = {Fragestellung: Die Kompensation von atembedingten Tumorbewegungen während der extrakraniellen stereotaktischen Strahlentherapie ist nicht trivial. Beim robotergestützten CyberKnife, einem immer häufiger eingesetzten aktive Bewegungskompensationssystem, werden dabei interne Tumorbewegungen mit optischen Marker auf der Brust korreliert und während der Behandlung mittels Prädiktionsalgorithmen vorhergesagt, um den Roboter synchron mit dem Tumor bewegen zu können. Genauigkeitsanalysen des Systems wurden mehrfach publiziert. Wir untersuchten nun, in wie weit die Prädiktionsgenauigkeit von der Atemphase abhängig ist, um Fehler besser bewerten und Patienten besser auf die Behandlung vorbereiten zu können. Methodik: Für die Analyse verwendeten wir Patientendaten von 37 Abdominellen- und 34 Lungenbehandlungen \(143 bzw. 124 Fraktionen\), die in unserem Zentrum behandelt wurden. Zur Untersuchung des Prädiktionsfehlers unterteilten wir die gemessene und prädizierte Atembewegung jedes Patienten, die in den CyberKnife Log-Dateien gespeichert werden, in je 10 Atemphasen. Anschließend berechneten wir die mittleren und maximalen Prädiktionsfehlers pro Phase in x, y und z Richtung und in 3D und verglichen die jeweiligen Atemphasen miteinander. Ergebnis: Der Median für den mittleren 3D Prädiktionsfehler für alle Leberpatienten betrug exemplarisch 0.14 mm \(Phase 1\), 0.09 mm \(Phase 3\), 0.07 mm \(Phase 5\) und 0.08 mm \(Phase 8\). Für Lungenpatienten beträgt der Median exemplarisch 0.05 mm \(Phase 1\), 0.03 mm \(Phase 3\), 0.03 mm \(Phase 5\) und 0.03 mm \(Phase 8\). Im Mittel sind die Prädiktionsfehler für Leberpatienten größer als für Lungenpatienten, was auf die größeren Bewegungen in der Leber zurückzuführen sein mag. Der maximale mittlere Prädiktionsfehler für einen Leberpatienten beträgt 0.7 mm \(Phase 1\) und für einen Lungenpatienten 0.81 mm \(Phase 3\). Die Ergebnisse zeigen, dass eine große Abhängigkeit zwischen Atemphasen und Prädiktionsfehler besteht. Der Prädiktionsfehler ist besonders an den Übergängen von Ex- zu Inspiration \(Phase 1/10\) größer als in den Phasen von reiner Inspiration \(Phase 3\), Exspirationen\ (Phase 8\) und Übergang von In- zu Exspiration \(Phase 5/6\). Diese Fehlerverteilung ist für Lungen und Leber Patienten gleich. Schlussfolgerung: Diese Analyse zeigt, dass der mittlere atemphasenabhängige Prädiktionsfehler für Lungen- und Leberpatienten zwar sehr klein ist, jedoch für einzelne Patienten dennoch teilweise hohe mittlere Prädiktionsfehler auftreten können. Dies ist vor allem dadurch bedingt, dass die Prädiktion der Atmungsbewegung nach längerer Ruhephase (Ausatmung) schwierig ist. Besseres Training der Patienten hinsichtlich einer regelmäßigen Atmung ohne Pause könnten hier Verbesserungen schaffen. Als nächsten Schritt planen wir den dosimetrischen Effekt für Patienten mit einem hohen Prädiktionsfehler genauer zu untersuchen. Zusätzlich soll das Potential von neueren Prädiktionsalgorithmen untersucht werden.} } @inproceedings{OOSWHD13a, Author = {S. Otte and C. Otte and A. Schlaefer and L. Wittig and G. Huttmann and D. Drömann and A. Zell}, Title = {OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks.}, Year = {(2013).}, Pages = {1-6}, Booktitle = {Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on}, Doi = {10.1109/MLSP.2013.6661944}, Url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6661944} } @COMMENT{Bibtex file generated on 2022-10-3 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications-1/2019-2013.html }