Segmentation on TVUS image having ambiguous boundaries and heterogeneous textures = 모호한 경계와 비균질한 텍스처를 가진 경질 초음파 영상에서의 분할

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Endometrial regions gives useful information to gynecologist for diagnosing or examining uterine, endometrium, and adnexal pathologies. To detect the endometrial lesions on TVUS, generally manual segmentation method is needed. It is labor-intensive and time-consuming. Moreover, delineating the border of endometrial region might be different from doctor to doctor due to the unclearness of boundary of TVUS image. Therefore, the automated and consistent guidance of finding endometrial region is beneficial. However, automated segmentation of endometrium is very challenging due to unclear boundary and heterogeneous texture. To tackle these issues, we propose a discriminator guided by endometrium key-point maps. The discriminator distinguishes a prediction map from a ground-truth segmentation map based on the key-point maps. The endometrium segmentation network strives to deceive the discriminator. In this adversarial way, the segmentation network can accurately find the boundary. Experimental results verify the performance of the proposed method on Samsung Medison dataset of the sagittal transvaginal ultrasound images.
Advisors
Ro, Yong Manresearcher노용만researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2019.2,[iii, 25 p. :]

Keywords

Transvaginal ultrasound (TUVS) image▼amedical image segmentation▼aendometrial region▼aadversarial learning▼akey-point guided discriminator; 자궁경질초음파▼a의료 영상 분할▼a자궁 내막▼a적대적 학습▼a키포인트 판별자

URI
http://hdl.handle.net/10203/266769
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843391&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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