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Polyp-size classification with RGB-D features for colonoscopy
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Paper 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.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095015 (13 March 2019); doi: 10.1117/12.2513093
Show Author Affiliations
Hayato Itoh, Nagoya Univ. (Japan)
Holger R. Roth, Nagoya Univ. (Japan)
Yuichi Mori, Showa Univ. Northern Yokohama Hospital (Japan)
Masashi Misawa, Showa Univ. Northern Yokohama Hospital (Japan)
Masahiro Oda, Nagoya Univ. (Japan)
Shin-ei Kudo, Showa Univ. Northern Yokohama Hospital (Japan)
Kensaku Mori, Nagoya Univ. (Japan)
National Institute of Informatics (Japan)


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

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