Dense-layer-based YOLO-v3 for detection and localization of colon perforations

Kai Jiang; Hayato Itoh; Masahiro Oda; Taishi Okumura; Yuichi Mori; Masashi Misawa; Takemasa Hayashi; Shin-Ei Kudo; Kensaku Mori

doi:10.1117/12.2582300

15 February 2021 Dense-layer-based YOLO-v3 for detection and localization of colon perforations

Kai Jiang, Hayato Itoh, Masahiro Oda, Taishi Okumura, Yuichi Mori, Masashi Misawa, Takemasa Hayashi, Shin-Ei Kudo, Kensaku Mori

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115971A (2021) https://doi.org/10.1117/12.2582300
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Endoscopic submucosal dissection is a minimally invasive treatment for early gastric cancer. In endoscopic submucosal dissection, a physician directly removes the mucosa around the lesion under internal endoscopy by using the flush knife. However, the flush knife may accidentally pierce the colonic wall and generate a perforation on it. If physicians overlooking a small perforation, a patient may need emergency open surgery, since a perforation can easily cause peritonitis. For the prevention of overlooking of perforations, a computer-aided diagnosis system has a potential demand. We believe automatic perforation detection and localization function is very useful for the analysis of endoscopic submucosal dissection videos for the development of a computeraided diagnosis system. At current stage, the research of perforation detection and localization progress slowly, automatic image-based perforation detection is very challenge. Thus, we devote to the development of detection and localization of perforations in colonoscopic videos. In this paper, we proposed a supervised-learning method for perforations detection and localization in colonoscopic videos. This method uses dense layers in YOLO-v3 instead of residual units, and a combination of binary cross entropy and generalized intersection over union loss as the loss function in the training process. This method achieved 0.854 accuracy, 0.850 AUC score and 0.884 mean average precision for perforation detection and localization, respectively, as an initial study

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Kai Jiang, Hayato Itoh, Masahiro Oda, Taishi Okumura, Yuichi Mori, Masashi Misawa, Takemasa Hayashi, Shin-Ei Kudo, and Kensaku Mori "Dense-layer-based YOLO-v3 for detection and localization of colon perforations", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115971A (15 February 2021); https://doi.org/10.1117/12.2582300

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