Quantitative polyp size measurements with photometric stereo endoscopy enhanced by deep learning (Conference Presentation)

Colorectal cancer is the second leading cause of cancer deaths in the United States. Identifying and removing premalignant lesions via colonoscopy can significantly reduce colorectal cancer mortality. Unfortunately, the protective value of screening colonoscopy is limited because more than one quarter of clinically-important lesions are missed on average. Most of these lesions are associated with characteristic 3D topographical shapes that appear subtle to a conventional colonoscope. Photometric stereo endoscopy captures this 3D structure but is inherently qualitative due to the unknown working distances from each point of the object to the endoscope. In this work, we use deep learning to estimate the depth from a monocular endoscope camera. Significant amounts of endoscopy data with known depth maps is required for training a convolutional neural network for deep learning. Moreover, this training problem is challenging because the colon texture is patient-specific and cannot be used to efficiently learn depth. To resolve these issues, we developed a photometric stereo endoscopy simulator and generated data with ground truth depths from a virtual, texture-free colon phantom. These data were used to train a deep convolutional neural field network that can estimate the depth for test data with an accuracy of 84%. We use this depth estimate to implement a smart photometric stereo algorithm that reconstructs absolute depth maps. Applying this technique to an in-vivo human colonoscopy video of a single polyp viewed at varying distance, initial results show a reduction in polyp size measurement variation from 15.5% with conventional to 3.4% with smart photometric reconstruction.