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Statistical Segmentation Models for Neuroimaging Data
Statistical Segmentation Models for Neuroimaging Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151132
- ISBN
- 9798382830759
- DDC
- 574
- 저자명
- Bae, Eunchan.
- 서명/저자
- Statistical Segmentation Models for Neuroimaging Data
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 87 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Shinohara, Russell T.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약Imaging biomarkers have been widely used for screening, diagnosing, and measuring the progression of disease in neuroradiology. However, quantifying these imaging biomarkers is costly, time-consuming, and often not reproducible. Here, we propose statistical segmentation models for neuroimaging data to address these issues and resolve the bottleneck in imaging research. Chapter 2 introduces a fast, free, consistent, and unsupervised beta-mixture oligodendrocyte segmentation system (FAST) that can segment and track oligodendrocytes in three-dimensional images over time with minimal human input. We demonstrate that the FAST model can segment and track oligodendrocytes similarly to a blinded human observer. For non-invasive neuroimaging data, Chapter 3 proposes an atlas-based normalization method that can improve the signal-to-noise ratio while reducing the inhomogeneous intensities of the thalamus in magnetic resonance imaging. With this normalization method, chapter 4 introduces an error-adjusting segmentation model that provides error-adjusted covariate effect estimates, as well as estimates for false-positive and false-negative rates of the training data. Through applications to multiple sclerosis thalamic lesion segmentation in magnetic resonance imaging, we showed that our proposed model offers better assistance in detecting thalamic lesions. These new statistical segmentation models can facilitate neuroradiology research and assist in diagnostic and disease monitoring application by providing robust, reliable, and reproducible results.
- 일반주제명
- Biostatistics
- 일반주제명
- Statistics
- 일반주제명
- Epidemiology
- 키워드
- Neuroradiology
- 키워드
- Machine learning
- 키워드
- Neuroimaging
- 기타저자
- University of Pennsylvania Statistics and Data Science
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798382830759
■035 ▼a(MiAaPQ)AAI31147699
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aBae, Eunchan.
■24510▼aStatistical Segmentation Models for Neuroimaging Data
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a87 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Shinohara, Russell T.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aImaging biomarkers have been widely used for screening, diagnosing, and measuring the progression of disease in neuroradiology. However, quantifying these imaging biomarkers is costly, time-consuming, and often not reproducible. Here, we propose statistical segmentation models for neuroimaging data to address these issues and resolve the bottleneck in imaging research. Chapter 2 introduces a fast, free, consistent, and unsupervised beta-mixture oligodendrocyte segmentation system (FAST) that can segment and track oligodendrocytes in three-dimensional images over time with minimal human input. We demonstrate that the FAST model can segment and track oligodendrocytes similarly to a blinded human observer. For non-invasive neuroimaging data, Chapter 3 proposes an atlas-based normalization method that can improve the signal-to-noise ratio while reducing the inhomogeneous intensities of the thalamus in magnetic resonance imaging. With this normalization method, chapter 4 introduces an error-adjusting segmentation model that provides error-adjusted covariate effect estimates, as well as estimates for false-positive and false-negative rates of the training data. Through applications to multiple sclerosis thalamic lesion segmentation in magnetic resonance imaging, we showed that our proposed model offers better assistance in detecting thalamic lesions. These new statistical segmentation models can facilitate neuroradiology research and assist in diagnostic and disease monitoring application by providing robust, reliable, and reproducible results.
■590 ▼aSchool code: 0175.
■650 4▼aBiostatistics
■650 4▼aStatistics
■650 4▼aEpidemiology
■653 ▼aImaging biomarkers
■653 ▼aNeuroradiology
■653 ▼aMachine learning
■653 ▼aNeuroimaging
■653 ▼aSegmentation models
■690 ▼a0308
■690 ▼a0463
■690 ▼a0800
■690 ▼a0766
■71020▼aUniversity of Pennsylvania▼bStatistics and Data Science.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160890▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
