Segmentation boundary guided adversarial learning for uterus landmark detection = 자궁 특징점 검출을 위한 분할 영상 경계 기반의 적대적 학습 방법

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Uterine cancer is the second frequent cancer related to women and increases steadily. The early detection and diagnose of uterine cancer can make patient be easily recovered. Therefore, the early detection of uterine cancer is very important. For early detection of uterine cancer, experts check the length and thickness of uterus. However, due to fuzzy image quality and heterogeneous texture, the ultrasound image analysis is challenging. Especially, in the case of uterus, the shape and textures of uterus varies with menstrual cycle. Therefore, accurately diagnose with ultrasound image is too time consuming and need well trained experts. In this thesis, we propose segmentation boundary guided adversarial learning for uterus landmark detection. For the adversarial learning, the predictor predicts uterus landmark points. Then the discriminators discriminate whether given landmark points are correct or not with segmentation image. Through the adversarial learning, the landmark detection accuracy improved effectively.
Advisors
Ro, Yong Manresearcher노용만researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2018.8,[iii, 18 p. :]

Keywords

Landmark detection▼aultrasound uterus image▼agenerative adversarial network; 특징점 검출; 초음파 자궁 영상▼a적대적 학습 방법

URI
http://hdl.handle.net/10203/266766
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=828581&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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