Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning
dc.contributor.author | Rahman, Md Marufi | |
dc.contributor.author | Oh, JungHwan | |
dc.contributor.author | Tavanapong, Wallapak | |
dc.contributor.author | Wong, Johnny | |
dc.contributor.author | de Groen, Piet C. | |
dc.contributor.department | Computer Science | |
dc.date.accessioned | 2022-02-08T15:17:51Z | |
dc.date.available | 2022-02-08T15:17:51Z | |
dc.date.issued | 2022-01-01 | |
dc.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. | |
dc.description.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. | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/Nr1VMdAz | |
dc.language.iso | en | |
dc.publisher | © Springer Nature Switzerland AG 2021 | |
dc.title | Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning | |
dc.type | Presentation | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 5b8e3e14-3847-4a36-aa1e-0782ced64a70 | |
relation.isAuthorOfPublication | f9b67a19-5d18-4682-9a80-4f91f92018a2 | |
relation.isOrgUnitOfPublication | f7be4eb9-d1d0-4081-859b-b15cee251456 |