Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy [chapter]

Xingtong Liu, Ayushi Sinha, Mathias Unberath, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Austin Reiter
2018 Lecture Notes in Computer Science  
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires sequential data from monocular endoscopic videos and a multi-view stereo reconstruction method, e.g. structure from motion, that supervises learning in a sparse but accurate manner. Consequently, our method requires neither manual interaction, such as scaling or labeling, nor patient CT
more » ... n the training and application phases. We demonstrate the performance of our method on sinus endoscopy data from two patients and validate depth prediction quantitatively using corresponding patient CT scans where we found submillimeter residual errors.
doi:10.1007/978-3-030-01201-4_15 fatcat:ce4fy2lr25f3fgdogsiftvqm2i