서브메뉴
검색
Computer Vision for Morphological Evaluation of Musculoskeletal Disorders in Magnetic Resonance Imaging- [electronic resource]
Computer Vision for Morphological Evaluation of Musculoskeletal Disorders in Magnetic Resonance Imaging- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100436
- ISBN
- 9798379621025
- DDC
- 610
- 저자명
- Gao, Kenneth.
- 서명/저자
- Computer Vision for Morphological Evaluation of Musculoskeletal Disorders in Magnetic Resonance Imaging - [electronic resource]
- 발행사항
- [S.l.]: : University of California, San Francisco., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(174 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Majumdar, Sharmila.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Francisco, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약With the aging of the general population, musculoskeletal (MSK) diseases have moved to the forefront of healthcare concerns and are the leading causes of disability globally. Noninvasive imaging is routinely utilized in the clinic to diagnose and monitor onset and progression of MSK conditions. However, due to the qualitative nature of imaging assessments and increasing labor costs of evaluating advanced imaging modalities, there is a crucial need for automatic quantitative approaches. In this dissertation, we explore the development of computer vision techniques for extracting morphological features associated with low back pain and knee osteoarthritis, two of the most prevalent and debilitating MSK conditions.We begin by addressing the costs of image annotation via automation with deep learning. More specifically, we developed convolutional neural networks for two purposes: (1) to semantically segment various tissues, allowing for geometric tissue characterization, and (2) to detect and localize lesions and abnormalities. Then, leveraging these models for feature extraction, we harmonized tissue geometries in 3D Euclidean space using atlas-based registration to identify tissue shapes predisposed to disease onset. These techniques were applied to both large-scale and small, limited datasets, demonstrating the utility of computer vision techniques for morphological evaluation in a data-driven, exploratory manner.
- 일반주제명
- Bioengineering.
- 일반주제명
- Medical imaging.
- 일반주제명
- Health care management.
- 키워드
- Computer vision
- 키워드
- Low back pain
- 키워드
- Machine learning
- 키워드
- Osteoarthritis
- 기타저자
- University of California, San Francisco Bioengineering
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016932270
■00520240214100436
■006m o d
■007cr#unu||||||||
■020 ▼a9798379621025
■035 ▼a(MiAaPQ)AAI30490773
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aGao, Kenneth.▼0(orcid)0000-0002-5975-0127
■24510▼aComputer Vision for Morphological Evaluation of Musculoskeletal Disorders in Magnetic Resonance Imaging▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, San Francisco. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(174 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Majumdar, Sharmila.
■5021 ▼aThesis (Ph.D.)--University of California, San Francisco, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aWith the aging of the general population, musculoskeletal (MSK) diseases have moved to the forefront of healthcare concerns and are the leading causes of disability globally. Noninvasive imaging is routinely utilized in the clinic to diagnose and monitor onset and progression of MSK conditions. However, due to the qualitative nature of imaging assessments and increasing labor costs of evaluating advanced imaging modalities, there is a crucial need for automatic quantitative approaches. In this dissertation, we explore the development of computer vision techniques for extracting morphological features associated with low back pain and knee osteoarthritis, two of the most prevalent and debilitating MSK conditions.We begin by addressing the costs of image annotation via automation with deep learning. More specifically, we developed convolutional neural networks for two purposes: (1) to semantically segment various tissues, allowing for geometric tissue characterization, and (2) to detect and localize lesions and abnormalities. Then, leveraging these models for feature extraction, we harmonized tissue geometries in 3D Euclidean space using atlas-based registration to identify tissue shapes predisposed to disease onset. These techniques were applied to both large-scale and small, limited datasets, demonstrating the utility of computer vision techniques for morphological evaluation in a data-driven, exploratory manner.
■590 ▼aSchool code: 0034.
■650 4▼aBioengineering.
■650 4▼aMedical imaging.
■650 4▼aHealth care management.
■653 ▼aComputer vision
■653 ▼aLow back pain
■653 ▼aMachine learning
■653 ▼aMagnetic resonance imaging
■653 ▼aMusculoskeletal conditions
■653 ▼aOsteoarthritis
■690 ▼a0202
■690 ▼a0574
■690 ▼a0769
■71020▼aUniversity of California, San Francisco▼bBioengineering.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0034
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932270▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
