Publisher DOI: 10.1117/12.2581023
Title: In-vivo markerless motion detection from volumetric optical coherence tomography data using CNNs
Language: English
Authors: Sprenger, Johanna 
Neidhardt, Maximilian 
Schlüter, Matthias 
Latus, Sarah  
Gosau, Tobias 
Kemmling, Julia 
Feldhaus, Susanne 
Schumacher, Udo 
Schlaefer, Alexander 
Issue Date: 2021
Publisher: SPIE
Source: Image-Guided Procedures, Robotic Interventions, and Modeling: SPIE Medical Imaging (2021)
Abstract (english): 
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
Conference: SPIE Medical Imaging, 2021 
ISBN: 978-1-51064-025-2
Institute: Medizintechnische und Intelligente Systeme E-1 
Document Type: Chapter/Article (Proceedings)
Project: Robotisierte Ultraschall-gestützte Bildgebung zur Echtzeit-Bewegungskompensation in der Strahlentherapie (RobUST), Phase II 
Funded by: Deutsche Forschungsgemeinschaft (DFG) 
More Funding information: This work was partially funded by the i3 initiative of the Hamburg University of Technology and by the German Research Foundation (DFG, grant number SCHL 1844/2-2).
Part of Series: 
Volume number: 11598
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