자료유형  

 학위논문

Control Number  

0016932270

International Standard Book Number  

9798379621025

Dewey Decimal Classification Number  

610

Main Entry-Personal Name  

Gao, Kenneth.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, San Francisco., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(174 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Majumdar, Sharmila.

Dissertation Note  

Thesis (Ph.D.)--University of California, San Francisco, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

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.

Subject Added Entry-Topical Term  

Bioengineering.

Subject Added Entry-Topical Term  

Medical imaging.

Subject Added Entry-Topical Term  

Health care management.

Index Term-Uncontrolled  

Computer vision

Index Term-Uncontrolled  

Low back pain

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Magnetic resonance imaging

Index Term-Uncontrolled  

Musculoskeletal conditions

Index Term-Uncontrolled  

Osteoarthritis

Added Entry-Corporate Name  

University of California, San Francisco Bioengineering

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:639338