Recent Development of Computer Vision Technology to Improve Capsule Endoscopyopen access
- Authors
- Park, Junseok; Hwang, Youngbae; Yoon, Ju-Hong; Park, Min-Gyu; Kim, Jungho; Lim, Yun Jeong; Chun, Hoon Jai
- Issue Date
- Jul-2019
- Publisher
- 대한소화기내시경학회
- Keywords
- Capsule endoscopy; Computer vision technology; Deep learning
- Citation
- Clinical Endoscopy, v.52, no.4, pp 328 - 333
- Pages
- 6
- Journal Title
- Clinical Endoscopy
- Volume
- 52
- Number
- 4
- Start Page
- 328
- End Page
- 333
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/4418
- DOI
- 10.5946/ce.2018.172
- ISSN
- 2234-2400
2234-2443
- 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.
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Collections - College of Medicine > Department of Internal Medicine > 1. Journal Articles
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