Clinical Endoscopy

  • 제52권4호
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  • Pages.328-333
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  • 2019
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  • 2234-2400(pISSN)
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  • 2234-2443(eISSN)
대한소화기내시경학회 (The Korean Society of Gastrointestinal Endoscopy)
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Recent Development of Computer Vision Technology to Improve Capsule Endoscopy

  • Park, Junseok (Digestive Disease Center, Institute for Digestive Research, Department of Internal Medicine, Soonchunhyang University College of Medicine) ;
  • Hwang, Youngbae (Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI)) ;
  • Yoon Ju-Hong (Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI)) ;
  • Park, Min-Gyu (Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI)) ;
  • Kim, Jungho (Intelligent Image Processing Research Center, Korea Electronics Technology Institute (KETI)) ;
  • Lim, Yun Jeong (Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine) ;
  • Chun, Hoon Jai (Division of Gastroenterology and Hepatology, Department of Internal Medicine, Institute of Gastrointestinal Medical Instrument Research, Korea University College of Medicine)
  • 투고 : 2018.10.08
  • 심사 : 2018.11.25
  • 발행 : 2019.07.31
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

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