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13 March 2019 Polyp-size classification with RGB-D features for colonoscopy
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Abstract
Measurement of a polyp size is an essential task in colon cancer screening, since the polyp-size information has critical roles for decision on colonoscopy. However, an estimation of a polyp size from a single view of colonoscope without a measurement device is quite difficult even for expert physicians. To overcome this difficulty, automated size estimation techniques would be desirable for clinical scenes. This paper presents polyp-size classification method with a single colonoscopic image for colonoscopy. Our proposed method estimates depth information from a single colonoscopic image with trained model and utilises the estimated information for the classification. In our method, the model for depth information is obtained by deep learning with colonoscopic videos. Experimental results show the achievement of binary and trinary polyp-size classification with 79% and 74% accuracy from a single still image of a colonoscopic movie.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hayato Itoh, Holger R. Roth, Yuichi Mori, Masashi Misawa, Masahiro Oda, Shin-ei Kudo, and Kensaku Mori "Polyp-size classification with RGB-D features for colonoscopy", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095015 (13 March 2019); https://doi.org/10.1117/12.2513093
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