Recent development of computer vision technology to improve capsule endoscopy

Junseok Park, Youngbae Hwang, Ju Hong Yoon, Min Gyu Park, Jungho Kim, Yun Jeong Lim, Hoon-Jai Chun

Research output: Contribution to journalReview article

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.

Original languageEnglish
Pages (from-to)328-333
Number of pages6
JournalClinical Endoscopy
Volume52
Issue number4
DOIs
Publication statusPublished - 2019 Jul 1

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Keywords

  • Capsule endoscopy
  • Computer vision technology
  • Deep learning

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Radiology Nuclear Medicine and imaging
  • Gastroenterology

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