Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning
Date
2022-01-01
Authors
Rahman, Md Marufi
Oh, JungHwan
Tavanapong, Wallapak
Wong, Johnny
de Groen, Piet C.
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
© Springer Nature Switzerland AG 2021
Authors
Research Projects
Organizational Units
Organizational Unit
Journal Issue
Is Version Of
Versions
Series
Department
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.
Comments
Description
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.