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Recent development of computer vision technology to improve capsule endoscopy

Authors
Park J.Hwang Y.Yoon J.-H.Park M.-G.Kim J.Lim Y.J.Chun H.J.
Issue Date
2019
Publisher
Korean Society of Gastrointestinal Endoscopy
Keywords
Capsule endoscopy; Computer vision technology; Deep learning
Citation
Clinical Endoscopy, v.52, no.4, pp.328 - 333
Indexed
SCOPUS
KCI
Journal Title
Clinical Endoscopy
Volume
52
Number
4
Start Page
328
End Page
333
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/2699
DOI
10.5946/ce.2018.172
ISSN
2234-2400
Abstract
Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed. © 2019 Korean Society of Gastrointestinal Endoscopy.
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Anam Hospital (Department of Gastroenterology and Hepatology, Anam Hospital)
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