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

Md Marufi Rahman, Jung Hwan Oh, Wallapak Tavanapong, Johnny Wong, Piet C. de Groen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 colonoscopy automatically to assist the endoscopist to improve the quality of the actual procedure being performed. One of the fundamental steps for the automated quality 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 endoscopy. 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 Convolutional 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.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 16th International Symposium, ISVC 2021, Proceedings
EditorsGeorge Bebis, Vassilis Athitsos, Tong Yan, Manfred Lau, Frederick Li, Conglei Shi, Xiaoru Yuan, Christos Mousas, Gerd Bruder
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030904357
StatePublished - 2021
Event16th International Symposium on Visual Computing, ISVC 2021 - Virtual Online
Duration: Oct 4 2021Oct 6 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13018 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Visual Computing, ISVC 2021
CityVirtual Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Bite-block
  • Colonoscopy
  • Convolutional neural network
  • Image processing
  • Object detection
  • Upper endoscopy


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