Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning

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2022-01-01
Authors
Rahman, Md Marufi
Oh, JungHwan
Tavanapong, Wallapak
Wong, Johnny
de Groen, Piet C.
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© Springer Nature Switzerland AG 2021
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Computer Science
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Computer Science
Abstract
Colonoscopy is currently the gold standard procedure for colorectal cancer (CRC) screening. However, the dominant explanations for the continued incidence of CRC are endoscopist-related factors. To address this, we have been investigating an automated feedback system which measures quality of colonos-copy automatically to assist the endoscopist to improve the quality of the actual procedure being performed. One of the fundamental steps for the automated qual-ity feedback system is to distinguish a colonoscopy from an upper endoscopy since upper endoscopy and colonoscopy procedures are performed in the same room at different times, and it is necessary to distinguish the type of a procedure prior to execution of any quality measurement method to evaluate the procedure. In upper endoscopy, a bite-block is inserted for patient protection. By detecting this bite-block appearance, we can distinguish colonoscopy from upper endos-copy. However, there are various colors (i.e., blue, green, white, etc.) of bite-blocks. Our solution utilizes analyses of Hue and Saturation values and two Con-volutional Neural Networks (CNNs). One CNN detects image patches of a bite-block regardless of its colors. The other CNN detects image patches of the tongue. The experimental results show that the proposed solution is highly promising.
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This is a manuscript of a proceeding published as Rahman M.M., Oh J., Tavanapong W., Wong J., de Groen P.C. (2021) Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science, vol 13018. Springer, Cham. doi:10.1007/978-3-030-90436-4_17.
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