Download this list as BibTeX file.

2021

  • F. N. Schmidt and S. Gerlach and M. Issleib and A. Schlaefer and B. Busse (2021). Development of a virtual reality-based training for the elderly with increased fracture risk to prevent falls and improve their balance. Bone Reports. 14 100950. [doi] [www] [BibTex]

  • J. F. Fast and H. R. Dava and A. K. Rüppel and D. Kundrat and M. Krauth and M.-H. Laves and S. Spindeldreier and L. A. Kahrs and M. Ptok (2021). Stereo Laryngoscopic Impact Site Prediction for Droplet-Based Stimulation of the Laryngeal Adductor Reflex. IEEE Access. 9 112177-112192. [Abstract] [doi] [BibTex]

  • J. Ohlsen and M. Neidhardt and A. Schlaefer and N. Hoffmann (2021). Modelling shear wave propagation in soft tissue surrogates using a finite element- and finite difference method. PAMM. 20 (1), e202000148. [Abstract] [doi] [www] [BibTex]

  • J. Sprenger and J. Petersen and N. Neumann and H. Reichenspurner and D. Russ and C. Detter and A. Schlaefer (2021). Tracking heart surface features to determine myocardial contrast agent enrichment:. Current Directions in Biomedical Engineering. 7 (1), 53-57. [Abstract] [doi] [www] [BibTex]

  • J. Sprenger and M. Neidhardt and M. Schlüter and S. Latus and T. Gosau and J. Kemmling and S. Feldhaus and U. Schumacher and A. Schlaefer (2021). In-vivo markerless motion detection from volumetric optical coherence tomography data using CNNs. In Cristian A. Linte and Jeffrey H. Siewerdsen (Eds.) Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling SPIE: 345 - 350. [Abstract] [doi] [www] [BibTex]

  • J. Sprenger and T. Saathoff and A. Schlaefer (2021). Automated robotic surface scanning with optical coherence tomography. IEEE International Symposium on Biomedical Imaging accepted. [Abstract] [BibTex]

  • K. Linka and M. Hillgärtner and K. P. Abdolazizi and R. C. Aydin and M. Itskov and C. J. Cyron (2021). Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning. Journal of Computational Physics. 429 110010. [Abstract] [doi] [www] [BibTex]

  • K. P. Abdolazizi and K. Linka and J. Sprenger and M. Neidhardt and A. Schlaefer and C. J. Cyron (2021). Concentration-Specific Constitutive Modeling of Gelatin Based on Artificial Neural Networks. PAMM. 20 (1), e202000284. [Abstract] [doi] [www] [BibTex]

  • K. P. Abdolazizi and K. Linka and J. Sprenger and M. Neidhardt and A. Schlaefer and C. J. Cyron (2021). Concentration-Specific Constitutive Modeling of Gelatin Based on Artificial Neural Networks. PAMM. 20 (1), e202000284. [Abstract] [doi] [www] [BibTex]

  • L. Bargsten and D. Klisch and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and M. Grass and S. Blankenberg and M. Seiffert and A. Schlaefer (2021). Deep learning for guidewire detection in intravascular ultrasound images:. Current Directions in Biomedical Engineering. 7 (1), 106-110. [Abstract] [doi] [www] [BibTex]

  • L. Bargsten and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and J. Sprenger and M. Grass and M. Seiffert and S. Blankenberg and A. Schlaefer (2021). Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks. In Brett C. Byram and Nicole V. Ruiter (Eds.) Medical Imaging 2021: Ultrasonic Imaging and Tomography SPIE: 1-7. [Abstract] [doi] [www] [BibTex]

  • L. Bargsten and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and M. Grass and S. Blankenberg and M. Seiffert and A. Schlaefer (2021). Deep learning for calcium segmentation in intravascular ultrasound images:. Current Directions in Biomedical Engineering. 7 (1), 96-100. [Abstract] [doi] [www] [BibTex]

  • L. Bargsten and K. A. Riedl and T. Wissel and F. J. Brunner and K. Schaefers and M. Grass and S. Blankenberg and M. Seiffert and A. Schlaefer (2021). Attention via Scattering Transforms for Segmentation of Small Intravascular Ultrasound Data Sets. In Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris (Eds.) Proceedings of the Fourth Conference on Medical Imaging with Deep Learning PMLR: 34-47. [Abstract] [www] [BibTex]

  • L. Bargsten and S. Raschka and A. Schlaefer (2021). Capsule networks for segmentation of small intravascular ultrasound image datasets. International Journal of Computer Assisted Radiology and Surgery. 16 (8), 1243-1254. [Abstract] [doi] [www] [BibTex]

  • M. Bengs and S. Pant and M. Bockmayr and U. Schüller and A. Schlaefer (2021). Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning. Current Directions in Biomedical Engineering. 7 (1), 63-66. [Abstract] [doi] [www] [BibTex]

  • M. Neidhardt and J. Ohlsen and N. Hoffmann and A. Schlaefer (2021). Parameter Identification for Ultrasound Shear Wave Elastography Simulation:. Current Directions in Biomedical Engineering. 7 (1), 35-38. [Abstract] [doi] [www] [BibTex]

  • R. Mieling and J. Sprenger and S. Latus and L. Bargsten and A. Schlaefer (2021). A novel optical needle probe for deep learning-based tissue elasticity characterization:. Current Directions in Biomedical Engineering. 7 (1), 21-25. [Abstract] [doi] [www] [BibTex]

  • S. Latus and J. Sprenger and M. Neidhardt and J. Schadler and A. Ron and A. Fitzek and M. Schlüter and P. Breitfeld and A. Heinemann and K. Püschel and A. Schlaefer (2021). Rupture detection during needle insertion using complex OCT data and CNNs. IEEE Transactions on Biomedical Engineering. 68 (10), 3059-3067. [Abstract] [doi] [BibTex]

2020

  • A. Rogalla and S. Lehmann and M. Neidhardt and J. Sprenger and M. Bengs and A. Schlaefer and S. Schupp (2020). Synthesizing Strategies for Needle Steering in Gelatin Phantoms. Models for Formal Analysis of Real Systems (MARS 2020) [Abstract] [doi] [www] [BibTex]

  • A. Rogalla and T. Kamph and U. Bulmann and K. Billerbeck and M. Blumreiter and S. Schupp (2020). Designing And Analyzing Open Application-Oriented Labs in Software-Verification Education. Annual Conference of European Society for Engineering Education (SEFI). Enschede (the Netherlands) 444-453. [Abstract] [BibTex]

  • D.B. Ellebrecht and S. Latus and A. Schlaefer and T. Keck and N. Gessert (2020). Towards an Optical Biopsy during Visceral Surgical Interventions. Visceral Medicine. [Abstract] [doi] [BibTex]

  • F. Behrendt and N. Gessert and A. Schlaefer (2020). Generalization of spatio-temporal deep learning for vision-based force estimation. Current Directions in Biomedical Engineering. 6 (1), 20200024. [Abstract] [doi] [www] [BibTex]

  • F. Griese AND S. Latus AND M. Schlüter AND M. Graeser AND M. Lutz AND A. Schlaefer AND T. Knopp (2020). In-Vitro MPI-guided IVOCT catheter tracking in real time for motion artifact compensation. PLOS ONE. 15 (3), e0230821. [Abstract] [doi] [www] [BibTex]

  • J. Krüger and R. Opfer and N. Gessert and A.-C. Ostwaldt and P. Manogaran and H. H. Kitzler and A. Schlaefer and S. Schippling (2020). Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks. NeuroImage: Clinical. 28 102445. [Abstract] [doi] [www] [BibTex]

  • L. Bargsten and A. Schlaefer (2020). SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. International Journal of Computer Assisted Radiology and Surgery. 15 (9), 1427-1436. [Abstract] [doi] [www] [BibTex]

  • M. Bengs and N. Gessert and A. Schlaefer (2020). A Deep Learning Approach for Motion Forecasting Using 4D OCT Data. International Conference on Medical Imaging with Deep Learning accepted. [Abstract] [www] [BibTex]

  • M. Bengs and N. Gessert and M. Schlüter and A. Schlaefer (2020). Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data. International Journal of Computer Assisted Radiology and Surgery. 15 (6), 943-952. [Abstract] [doi] [www] [BibTex]

  • M. Bengs and N. Gessert and W. Laffers and D. Eggert and S. Westermann and N.A. Mueller and A.O.H. Gerstners and C. Betz and A. Schlaefer (2020). Spectral-spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification. Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 Springer International Publishing: Cham 690-699. [Abstract] [BibTex]

  • M. Bengs and S. Westermann and N. Gessert and D. Eggert and A. O. H. Gerstner, N. A. Mueller and C. Betz and W. Laffers and A. Schlaefer (2020). Spatio-spectral deep learning methods for in-vivohyperspectral laryngeal cancer detection. SPIE Medical Imaging 2020: Computer-Aided Diagnosis in print. [BibTex]

  • M. Bengs and T. Gessert and A. Schlaefer (2020). 4D spatio-temporal convolutional networks for object position estimation in OCT volumes. Current directions in biomedical engineering. 6 (1), 20200001. [Abstract] [doi] [www] [BibTex]

  • M. Gromniak and M. Neidhardt and A. Heinemann and K. Püschel and A. Schlaefer (2020). Needle placement accuracy in CT-guided robotic post mortem biopsy. Current Directions in Biomedical Engineering. 6 (1), 20200031. [Abstract] [doi] [www] [BibTex]

  • M. Gromniak and N. Gessert and T. Saathoff and A. Schlaefer (2020). Needle tip force estimation by deep learning from raw spectral OCT data. International Journal of Computer Assisted Radiology and Surgery. 15 1699-1702. [Abstract] [doi] [www] [BibTex]

  • M. Neidhardt and M. Bengs and S. Latus and M. Schlüter and T. Saathoff and A. Schlaefer (2020). 4D Deep learning for real-time volumetric optical coherence elastography. International Journal of Computer Assisted Radiology and Surgery 2020 1861-6429. [Abstract] [doi] [www] [BibTex]

  • M. Neidhardt and M. Bengs and S. Latus and M. Schlüter and T. Saathoff and A. Schlaefer (2020). Deep Learning for High Speed Optical Coherence Elastography. IEEE International Symposium on Biomedical Imaging 1583-1586. [Abstract] [doi] [BibTex]

  • M. Neidhardt and N. Gessert and T. Gosau and J. Kemmling and S. Feldhaus and U. Schumacher and A. Schlaefer (2020). Force estimation from 4D OCT data in a human tumor xenograft mouse model. Current Directions in Biomedical Engineering. 6 (1), 20200022. [Abstract] [doi] [www] [BibTex]

  • M. Schlüter and L. Glandorf and J. Sprenger and M. Gromniak and M. Neidhardt and T. Saathoff and A. Schlaefer (2020). High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images. IEEE International Symposium on Biomedical Imaging 1979-1982. [Abstract] [doi] [BibTex]

  • M. Schlüter and L. Glandorf and M. Gromniak and T. Saathoff and A. Schlaefer (2020). Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography. Sensors. 20 (9), 2678. [Abstract] [doi] [BibTex]

  • M. Seemann and L. Bargsten and A. Schlaefer (2020). Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation. Current Directions in Biomedical Engineering. 6 (1), 20200015. [Abstract] [doi] [www] [BibTex]

  • N. Gessert (2020). Deep learning with multi-dimensional medical image data. TUHH Open Research: Hamburg, Germany [Abstract] [doi] [www] [BibTex]

  • N. Gessert and A. Schlaefer (2020). Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs. arXiv e-prints. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM@MICCAI 2019. Lecture Notes in Computer Science. 375-383. [Abstract] [doi] [www] [BibTex]

  • N. Gessert and J. Krüger and R. Opfer and A.-C. Ostwaldt and P. Manogaran and H. H. Kitzler and S. Schippling and A. Schlaefer (2020). Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs. Computerized Medical Imaging and Graphics. 84 (101772), [Abstract] [doi] [www] [BibTex]

  • N. Gessert and M. Bengs and A. Schlaefer (2020). Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models. SPIE Medical Imaging 2020 in print. [Abstract] [www] [BibTex]

  • N. Gessert and M. Bengs and J. Krüger and R. Opfer and A.-C. Ostwaldt and P. Manogaran and S. Schippling and A. Schlaefer (2020). 4D Deep Learning for Multiple-Sclerosis Lesion Activity Segmentation. Medical Imaging with Deep Learning accepted. [Abstract] [www] [BibTex]

  • N. Gessert and M. Bengs and M. Schlüter and A. Schlaefer (2020). Deep learning with 4D spatio-temporal data representations for OCT-based force estimation. Medical Image Analysis. 64 (101730), [Abstract] [doi] [www] [BibTex] [pmid]

  • N. Gessert and M. Nielsen and M. Shaikh and R. Werner and A. Schlaefer (2020). Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX. 7 100864. [Abstract] [doi] [www] [BibTex]

  • N. Gessert and T. Sentker and F. Madesta and R. Schmitz and H. Kniep and I. Baltruschat and R. Werner and A. Schlaefer (2020). Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting. IEEE Transactions on Biomedical Engineering. 67 (2), 495-503. [Abstract] [doi] [www] [BibTex]

  • R. Mieling and S. Latus and N. Gessert and M. Lutz and A. Schlaefer (2020). Deep learning-based rotation frequency estimation and NURD correction for IVOCT image data. (Suppl1) International Journal of CARS'2020. 15 (1), 162-163. [Abstract] [doi] [BibTex]

  • S. Gerlach and C. Fürweger and T. Hofmann and A. Schlaefer (2020). Multicriterial CNN based beam generation for robotic radiosurgery of the prostate. Current Directions in Biomedical Engineering. 6 (1), 20200030. [Abstract] [doi] [www] [BibTex]

  • S. Gerlach and C. Fürweger and T. Hofmann and A. Schlaefer (2020). Feasibility and analysis of CNN-based candidate beam generation for robotic radiosurgery. Medical Physics. 47 (9), 3806-3815. [Abstract] [doi] [www] [BibTex]

  • S. Gerlach and F. Siebert and A. Schlaefer (2020). BReP-SNAP-T-54: Efficient Stochastic Optimization Accounting for Uncertainty in HDR Prostate Brachytherapy Needle Placement. Medical Physics. 47 (6), e458. [Abstract] [doi] [www] [BibTex]

  • S. Latus and P. Breitfeld and M. Neidhardt and W. Reip and C. Zöllner and A. Schlaefer (2020). Boundary prediction during epidural punctures based on OCT relative motion analysis. EUR J ANAESTH. 2020 (Volume 37 | e-Supplement 58 | June 2020), [Abstract] [BibTex]

2019

  • R. Buchert and J. Krüger and N. Gessert and W. Lehnert and I. Apostolova and S. Klutmann and A. Schlaefer (2019). Deep Learning in SPECT and PET of the brain. [Abstract] [doi] [BibTex]

  • F. Sommer and L. Bargsten and A. Schlaefer (2019). IVUS-Simulation for Improving Segmentation Performance of Neural Networks via Data Augmentation. CURAC 2019 Tagungsband Reutlingen 47-51. [Abstract] [www] [BibTex]

  • 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 (2019). Fully Automated Longitudinal Segmentation of new or Enlarging Multiple Sclerosis (MS) Lesions Using 3D Convolutional Neural Networks. Clinical Neuroradiology. 29 (Suppl 1), 10. [www] [BibTex]

  • 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 (2019). Fully automated lesion segmentation using heavily trained 3D convolutional neural networks are equivalent to manual expert segmentations. Multiple Sclerosis Journal. 25 (2_suppl), 844-845. [doi] [www] [BibTex]

  • L. Bargsten and M. Wendebourg and A. Schlaefer (2019). Data Representations for Segmentation of Vascular Structures Using Convolutional Neural Networks with U-Net Architecture. In Proc. 2019 41st IEEE Engineering in Medicine and Biology Society (EMBC'19) Berlin, Germany 989-992. [Abstract] [doi] [www] [BibTex]

  • M. Bengs and N. Gessert and A. Schlaefer (2019). 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification. Proceedings of International Conference on Medical Imaging with Deep Learning Accepted. [Abstract] [www] [BibTex]

  • M. Gromniak and C. Brendes and A. Schlaefer (2019). A New Setup for Markerless Motion Compensation in TMS by Relative Head Tracking with a Small-Scale TOF Camera. CURAC 2019 Tagungsband Reutlingen 205-210. [Abstract] [www] [BibTex]

  • M. Schlüter and C. Fürweger and A. Schlaefer (2019). Optimizing Robot Motion for Robotic Ultrasound-Guided Radiation Therapy. Physics in Medicine & Biology. 64 (19), 195012. [Abstract] [doi] [BibTex]

  • M. Schlüter and C. Fürweger and A. Schlaefer (2019). Optimizing Configurations for 7-DoF Robotic Ultrasound Guidance in Radiotherapy of the Prostate. Annual International Conference of the IEEE Engineering in Medicine and Biology Society 6983-6986. [Abstract] [doi] [BibTex]

  • M. Schlüter and C. Otte and T. Saathoff and N. Gessert and A. Schlaefer (2019). Feasibility of a markerless tracking system based on optical coherence tomography. SPIE Medical Imaging accepted. [www] [BibTex]

  • 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 (2019). Towards OCT-Navigated Tissue Ablation with a Picosecond Infrared Laser (PIRL) and Mass-Spectrometric Analysis. Annual International Conference of the IEEE Engineering in Medicine and Biology Society 158-161. [Abstract] [doi] [BibTex]

  • M. Schlüter and S. Gerlach and C. Fürweger and A. Schlaefer (2019). Analysis and Optimization of the Robot Setup for Robotic-Ultrasound-Guided Radiation Therapy. International Journal of Computer Assisted Radiology and Surgery. 14 (8), 1379-1387. [Abstract] [doi] [BibTex]

  • M. Schlüter and S. Gerlach and C. Fürweger and A. Schlaefer (2019). Analysis and Optimization of the Robot Setup for Robotic-Ultrasound-Guided Radiation Therapy. Presented at International Congress and Exhibition on Computer Assisted Radiology and Surgery [BibTex]

  • M.H. Laves and S. Latus and J. Bergmeier and T. Ortmaier and L. A. Kahrs and A. Schlaefer (2019). Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery. International Journal of Computer Assisted Radiology and Surgery. 14 (1), 136-137. [doi] [www] [BibTex]

  • N. Gessert and A. Schlaefer (2019). Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation. International Conference on Medical Imaging with Deep Learning [Abstract] [www] [BibTex]

  • N. Gessert and L. Wittig and D. Drömann and T. Keck and A. Schlaefer and D. B. Ellebrecht (2019). Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks. CoRR. abs/1812.01464 [www] [BibTex]

  • N. Gessert and M. Bengs and L. Wittig and D. Drömann and T. Keck and A. Schlaefer and D. B. Ellebrecht (2019). Deep transfer learning methods for colon cancer classification in confocal laser microscopy images. International Journal of Computer Assisted Radiology and Surgery. [Abstract] [doi] [www] [BibTex]

  • N. Gessert and M. Gromniak and M. Bengs and L. Matthäus and A. Schlaefer (2019). Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels. CURAC 2019 Tagungsband Reutlingen 176-180. [Abstract] [www] [BibTex]

  • N. Gessert and M. Gromniak and M. Schlüter and A. Schlaefer (2019). Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation. CoRR. abs/1810.09582 [www] [BibTex]

  • N. Gessert and M. Lutz and M. Heyder and S. Latus and D. M. Leistner and Y. S. Abdelwahed and A. Schlaefer (2019). Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks. IEEE Transactions on Medical Imaging. 38 (2), 426-434. [Abstract] [doi] [BibTex]

  • N. Gessert and M. Nielsen and M. Shaikh and R. Werner and A. Schlaefer (2019). Skin Lesion Classification Using Loss Balancing, Ensembles of Multi-Resolution EfficientNets and Meta Data. ISIC Skin Lesion Classification Challenge @ MICCAI 2019 Shenzhen, China [www] [BibTex]

  • N. Gessert and M. Schlüter and S. Latus and V. Volgger and C. Betz and A. Schlaefer (2019). Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods. International Congress and Exhibition on Computer Assisted Radiology and Surgery accepted. [www] [BibTex]

  • N. Gessert and S. Latus and Y. S. Abdelwahed and D. M. Leistner and M. Lutz and A. Schlaefer (2019). Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization. CoRR. abs/1810.09578 [www] [BibTex]

  • 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 (2019). Spatio-temporal deep learning models for tip force estimation during needle insertion. International Journal of Computer Assisted Radiology and Surgery. [Abstract] [doi] [www] [BibTex]

  • N. Gessert and T. Sentker and F. Madesta and R. Schmitz and H. Kniep and I. Baltruschat and R. Werner and A. Schlaefer (2019). Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting. IEEE Transactions on Biomedical Engineering. Accepted. [Abstract] [doi] [www] [BibTex]

  • R. Chadda and S. Wismath and M. Hessinger and N. Schäfer and A. Schlaefer and M. Kupnik (2019). Needle Tip Force Sensor for Medical Applications. 2019 IEEE SENSORS 1-4. [Abstract] [doi] [BibTex]

  • S. Gerlach and M. Schlüter and C. Fürweger and A. Schlaefer (2019). Machbarkeit CNN-basierter Erzeugung von Kandidatenstrahlen für Radiochirurgie der Prostata. CURAC 2019 Tagungsband Reutlingen 128-129. [Abstract] [www] [BibTex]

  • S. Gerlach and M. Schlüter and C. Fürweger and A. Schlaefer (2019). Machbarkeit CNN-basierter Erzeugung von Kandidatenstrahlen für Radiochirurgie der Prostata. CURAC 2019 Tagungsband Reutlingen 128-129. [Abstract] [www] [BibTex]

  • 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 (2019). Bimodal intravascular volumetric imaging combining OCT and MPI. Medical Physics. 46 (3), 1371-1383. [Abstract] [doi] [www] [BibTex]

  • S. Latus and M. Neidhardt and M. Lutz and N. Gessert and N. Frey and A. Schlaefer (2019). Quantitative Analysis of 3D Artery Volume Reconstructions Using Biplane Angiography and Intravascular OCT Imaging. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 6004-6007. [Abstract] [doi] [BibTex]

  • S.-T. Antoni and S. Lehmann and S. Schupp and A. Schlaefer (2019). An Online Model Checking Approach to Soft-Tissue Detection for Rupture. CURAC 2019 Tagungsband Reutlingen 83-88. [Abstract] [www] [BibTex]

2018

  • 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 (2018). Stenosis analysis by synergizing MPI and intravascular OCT. International Workshop on Magnetic Particle Imaging 217-218. [BibTex]

  • J. Padberg and A. Schlaefer and S. Schupp (2018). Ein Ansatz zur nachvollziehbaren Verifikation medizinisch-cyber-physikalischer Systeme. In M. Tichy and E. Bodden and M. Kuhrmann and S. Wagner and J.-P. Steghöfer (Eds.) Software Engineering und Software Management 2018 Gesellschaft für Informatik: Bonn 209-210. [Abstract] [BibTex]

  • M. Wendebourg and O. Rajput and A. Schlaefer (2018). Detection of Simulated Clonic Seizures from Depth Camera Recordings. 6 (2), 88-94. [Abstract] [doi] [www] [BibTex]

  • N. Gessert and J. Beringhoff and C. Otte and A. Schlaefer (2018). Force Estimation from OCT Volumes using 3D CNNs. Int J Comput Assist Radiol Surg. 13 (7), 1073–1082. [Abstract] [doi] [www] [BibTex]

  • N. Gessert and M. Heyder and S. Latus and D. M. Leistner and Y. S. Abdelwahed and M. Lutz and A. Schlaefer (2018). Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification. International Conference on Medical Imaging with Deep Learning [Abstract] [www] [BibTex]

  • N. Gessert and M. Heyder and S. Latus and M. Lutz and A. Schlaefer (2018). Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning. (Suppl1) International Journal of CARS'2018. 13 (1), 99-100. [doi] [www] [BibTex]

  • N. Gessert and M. Schlüter and A. Schlaefer (2018). A Deep Learning Approach for Pose Estimation from Volumetric OCT Data. Medical Image Analysis. 46 162-179. [Abstract] [doi] [www] [BibTex]

  • 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 (2018). Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture - MICCAI 2018. International Conference on Medical Image Computing and Computer-Assisted Intervention 222-229, Spotlight Talk. [Abstract] [www] [BibTex]

  • N. Gessert and T. Sentker and F. Madesta and R. Schmitz and H. Kniep and I. Baltruschat and R. Werner and A. Schlaefer (2018). Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting. ArXiv e-prints. Oral. Best challenge submission with public data only. Overall 2nd placed team. [Abstract] [www] [BibTex]

  • O. Rajput* and N. Gessert* and M. Gromniak and L. Matthäus and A. Schlaefer (2018). Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks. 17. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboter Assistierte Chirurgie 138-141 *Shared First Authors . [Abstract] [www] [BibTex]

  • 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 (2018). Towards bimodal intravascular OCT MPI volumetric imaging. Proceedings of SPIE Medical Imaging: Physics of Medical Imaging 10573E. [Abstract] [doi] [www] [BibTex]

  • S. Lehmann and S.-T. Antoni and A. Schlaefer and S. Schupp (2018). A Quantitative Metric Temporal Logic for Execution-Time Constrained Verification. Model-Based Design of Cyber Physical Systems (CyPhy'18) Torino, Italy [BibTex]

  • 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 (2018). Model checking for trigger loss detection during Doppler ultrasound-guided fetal cardiovascular MRI. International Journal of Computer Assisted Radiology and Surgery. 13 (11), 1755-1766. [Abstract] [doi] [www] [BibTex]

  • T. Yu, F.-A. Siebert, A. Schlaefer (2018). A stochastic optimization approach accounting for uncertainty in HDR brachytherapy needle Placement. International Journal of Computer Assisted Radiology and Surgery CARS 2018. 13 (Suppl 1), 34-35. [Abstract] [doi] [www] [BibTex]

To top