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Polyp segmentation and classification using predicted depth from monocular endoscopy
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Paper Abstract

Colorectal cancer is the fourth leading cause of cancer deaths worldwide, the standard for detection and prevention is the identification and removal of premalignant lesions through optical colonoscopy. More than 60% of colorectal cancer cases are attributed to missed polyps. Current procedures for automated polyp detection are limited by the amount of data available for training, underrepresentation of non-polypoid lesions and lesions which are inherently difficult to label and do not incorporate information about the topography of the surface of the lumen. It has been shown that information related to depth and topography of the surface of the lumen can boost subjective lesion detection. In this work, we add predicted depth information as an additional mode of data when training deep networks for polyp detection, segmentation and classification. We use conditional GANs to predict depth from monocular endoscopy images and fuse these predicted depth maps with RGB white light images in feature space. Our empirical analysis demonstrates that we achieve state-of-the-art results with RGB-D polyp segmentation with a 98% accuracy on four different publically available datasets. Moreover, we demonstrate a 87.24% accuracy on lesion classification. We also show that our networks can domain adapt to a variety of different kinds of data from different sources.

Paper Details

Date Published: 15 March 2019
PDF: 5 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095011 (15 March 2019); doi: 10.1117/12.2513117
Show Author Affiliations
Faisal Mahmood, Johns Hopkins Univ. (United States)
Ziyun Yang, Johns Hopkins Univ. (United States)
Richard Chen, Johns Hopkins Univ. (United States)
Daniel Borders, Johns Hopkins Univ. (United States)
Wenhao Xu, Johns Hopkins Univ. (United States)
Nicholas J. Durr, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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