Learning-based fatty liver diagnosis method인공지능 기반의 지방간 진단 기법

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Absorption coefficient (AC) quantification methods in medical ultrasound (US) are effective for objective routine diagnosis of hepatic steatosis and liver disease. In this paper, we present various learning-based methods to quantify the biomechanical property of the tissue, and introduce clinical solutions for diagnosing hepatic steatosis and liver disease through these methods. First, a method for measuring the AC characteristics of the tissue within a user-specified area is presented. Second, a method to extract the AC of liver parenchyma insensitive to the measured ultrasound view is introduced. Third, a method of reconstructing the spatial characteristic of the target region into a two-dimensional image is suggested. The performance of the proposed methods are verified through simulation and clinical tests. These results demonstrate the clinical potential of the proposed learning-based quantification methods as a new standard for diagnosing hepatic steatosis and liver disease.
Advisors
Bae, Hyeon-Minresearcher배현민researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2022
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2022.8,[v, 41 p. :]

Keywords

Fatty liver▼aattenuation coefficient▼aquantitative ultrasound▼aabdominal ultrasound▼amedical ultrasound▼adeep neural network; 지방간 진단▼a감쇠 계수▼a정량적 초음파▼a복부 초음파▼a의료용 초음파▼a심층 신경망

URI
http://hdl.handle.net/10203/309127
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1007941&flag=dissertation
Appears in Collection
EE-Theses_Ph.D.(박사논문)
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