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Scaling Image Processing and Reconstruction to Whole Brains
Scaling Image Processing and Reconstruction to Whole Brains
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153040
- ISBN
- 9798346851134
- DDC
- 004
- 저자명
- Marrett, Karl.
- 서명/저자
- Scaling Image Processing and Reconstruction to Whole Brains
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 123 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Cong, Jason.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Neuronal reconstruction-a process that transforms image volumes into 3D geometries and skeletons of cells-bottlenecks the study of brain function, connectomics and pathology. Scientists need exact and complete segmentations to study subtle topological differences. Existing methods are disk-bound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons' shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting vision tasks such as segmentation and skeletonization into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67x the scale with 870x less memory while having 78% higher automated yield than APP2, the standard of performant reconstruction.
- 일반주제명
- Computer science
- 일반주제명
- Neurosciences
- 일반주제명
- Bioinformatics
- 일반주제명
- Bioengineering
- 키워드
- Proofreading
- 키워드
- Skeletonization
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798346851134
■035 ▼a(MiAaPQ)AAI31638582
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aMarrett, Karl.
■24510▼aScaling Image Processing and Reconstruction to Whole Brains
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a123 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Cong, Jason.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aNeuronal reconstruction-a process that transforms image volumes into 3D geometries and skeletons of cells-bottlenecks the study of brain function, connectomics and pathology. Scientists need exact and complete segmentations to study subtle topological differences. Existing methods are disk-bound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons' shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting vision tasks such as segmentation and skeletonization into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67x the scale with 870x less memory while having 78% higher automated yield than APP2, the standard of performant reconstruction.
■590 ▼aSchool code: 0031.
■650 4▼aComputer science
■650 4▼aNeurosciences
■650 4▼aBioinformatics
■650 4▼aBioengineering
■653 ▼aNeuronal reconstruction
■653 ▼aTopological accuracy
■653 ▼aProofreading
■653 ▼aSkeletonization
■690 ▼a0984
■690 ▼a0317
■690 ▼a0202
■690 ▼a0715
■71020▼aUniversity of California, Los Angeles▼bComputer Science 0201.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164747▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
