Title: In-vivo markerless motion detection from volumetric optical coherence tomography data using CNNs Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling
Written by: J. Sprenger and M. Neidhardt and M. Schl├╝ter and S. Latus and T. Gosau and J. Kemmling and S. Feldhaus and U. Schumacher and A. Schlaefer
in: 2021
Volume: 11598 Number:
on pages: 345 - 350
Editor: Cristian A. Linte and Jeffrey H. Siewerdsen
Publisher: SPIE
how published:
Organization: International Society for Optics and Photonics
DOI: 10.1117/12.2581023
URL: https://doi.org/10.1117/12.2581023

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Abstract: Precise navigation is an important task in robot-assisted and minimally invasive surgery. The need for optical markers and a lack of distinct anatomical features on skin or organs complicate tissue tracking with commercial tracking systems. Previous work has shown the feasibility of a 3D optical coherence tomography based system for this purpose. Furthermore, convolutional neural networks have been proven to precisely detect shifts between volumes. However, most experiments have been performed with phantoms or ex-vivo tissue. We introduce an experimental setup and perform measurements on perfused and non-perfused (dead) tissue of in-vivo xenograft tumors. We train 3D siamese deep learning models and evaluate the precision of the motion prediction. The network\'s ability to predict shifts for different motion magnitudes and also the performance for the different volume axes are compared. The root-mean-square errors are 0:12mm and 0:08mm on perfused and non-perfused tumor tissue, respectively

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