지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 328 - 333 (6page)
이용수
초록· 키워드
오류제보하기
Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capturea sparse number of images because of their mechanical limitations. Post-procedural management using computational methodscan enhance image quality. Additional information, including depth, can be obtained by using recently developed computer visiontechniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer visiontechnology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions moreaccurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditionalcomputerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving thediagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.
목차
등록된 정보가 없습니다.
참고문헌 (39)
참고문헌 신청
심기남 / 2017 / Quality Indicators for Small Bowel Capsule Endoscopy / Clinical Endoscopy 50(2):148~160
Iakovidis DK / 2015 / Software for enhanced video capsule endoscopy: challenges for essential progress / Nat Rev Gastroenterol Hepatol 12:172~186
Jia X / 2017 / Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features / 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) :3154~3157
Yuan Y / 2017 / Deep learning for polyp recognition in wireless capsule endoscopy images / Med Phys 44:1379~1389
Obukhova N / 2018 / Review of noise reduction methods and estimation of their effectiveness for medical endoscopic images processing / 2018 22nd Conference of Open Innovations Association (FRUCT) :204~210
Iakovidis DK / 2015 / Software for enhanced video capsule endoscopy: challenges for essential progress / Nat Rev Gastroenterol Hepatol 12:172~186
Jia X / 2017 / Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features / 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) :3154~3157
Yuan Y / 2017 / Deep learning for polyp recognition in wireless capsule endoscopy images / Med Phys 44:1379~1389
Obukhova N / 2018 / Review of noise reduction methods and estimation of their effectiveness for medical endoscopic images processing / 2018 22nd Conference of Open Innovations Association (FRUCT) :204~210
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