서브메뉴
검색
Automatic Vascular Model Construction From Medical Imaging Using Deep Learning
Automatic Vascular Model Construction From Medical Imaging Using Deep Learning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104719
- ISBN
- 9798293892372
- DDC
- 621
- 서명/저자
- Automatic Vascular Model Construction From Medical Imaging Using Deep Learning
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 120 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-04, Section: B.
- 주기사항
- Advisor: Shadden, Shawn C.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약Computational modeling of the cardiovascular system plays a vital role in understanding, diagnosing, and treating cardiovascular disease. However, traditional workflows for generating simulation-ready, patient-specific models are time-consuming, requiring extensive manual labor for geometric reconstruction and simulation setup. This dissertation introduces deep learning based methods designed to automate and accelerate the construction of image-based models to support hemodynamics simulation.First, we present SeqSeg (Sequential Segmentation), a novel deep learning method for automatic vascular segmentation. SeqSeg leverages a local U-Net-based architecture to iteratively track and segment vascular structures from medical imaging data. Compared to standard 2D and 3D global models such as nnU-Net, SeqSeg generates more complete vascular models and generalizes better to unannotated anatomy, enabling efficient geometric modeling from computed tomography (CT) and magnetic resonance (MR) data.Building upon this, we introduce MeshGrow, an integrated framework that combines automatic vascular and cardiac modeling to generate combined cardiovascular anatomies. MeshGrow can reconstruct both the heart and great vessels by employing a template deformation approach for the cardiac chambers and a step-wise growth-based method for vascular structures. The result is a simulation-ready mesh, including valve boundaries, constructed directly from medical images with minimal human intervention.In the third part of this work, we present MIROS (Medical Image to Reduced Order Simulation), a fully automated pipeline for performing reduced-order cardiovascular simulations. MIROS integrates SeqSeg-based geometry generation with reduced order modeling of blood flow and semi-automatic boundary condition assignment to produce hemodynamic simulations within minutes. This approach significantly reduces the computational and manual burden traditionally required, enabling rapid, patient-specific analyses and facilitating largescale studies.Finally, building on SeqSeg and inspired by advances in human trajectory forecasting, we propose VesselTrajNet, a novel method for vasculature tracking in medical images. By adapting a U-Net-based Gaussian heat map encoder-decoder architecture for multiple goal-driven path prediction, VesselTrajNet accurately models complex vascular branching without requiring explicit bifurcation detection. We demonstrate its utility on coronary artery CT data, underscoring its potential for diagnostic and interventional imaging.Together, these contributions advance the state of the art in automated cardiovascular modeling and simulation. By harnessing deep learning for the modeling pipeline, this work aims to make high-fidelity cardiovascular simulations more accessible, scalable, and clinically relevant.
- 일반주제명
- Mechanical engineering
- 일반주제명
- Computer science
- 일반주제명
- Medical imaging
- 키워드
- Deep learning
- 기타저자
- University of California, Berkeley Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017358556
■00520260202104719
■006m o d
■007cr#unu||||||||
■020 ▼a9798293892372
■035 ▼a(MiAaPQ)AAI32120909
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aSveinsson Cepero, Numi.
■24510▼aAutomatic Vascular Model Construction From Medical Imaging Using Deep Learning
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a120 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-04, Section: B.
■500 ▼aAdvisor: Shadden, Shawn C.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aComputational modeling of the cardiovascular system plays a vital role in understanding, diagnosing, and treating cardiovascular disease. However, traditional workflows for generating simulation-ready, patient-specific models are time-consuming, requiring extensive manual labor for geometric reconstruction and simulation setup. This dissertation introduces deep learning based methods designed to automate and accelerate the construction of image-based models to support hemodynamics simulation.First, we present SeqSeg (Sequential Segmentation), a novel deep learning method for automatic vascular segmentation. SeqSeg leverages a local U-Net-based architecture to iteratively track and segment vascular structures from medical imaging data. Compared to standard 2D and 3D global models such as nnU-Net, SeqSeg generates more complete vascular models and generalizes better to unannotated anatomy, enabling efficient geometric modeling from computed tomography (CT) and magnetic resonance (MR) data.Building upon this, we introduce MeshGrow, an integrated framework that combines automatic vascular and cardiac modeling to generate combined cardiovascular anatomies. MeshGrow can reconstruct both the heart and great vessels by employing a template deformation approach for the cardiac chambers and a step-wise growth-based method for vascular structures. The result is a simulation-ready mesh, including valve boundaries, constructed directly from medical images with minimal human intervention.In the third part of this work, we present MIROS (Medical Image to Reduced Order Simulation), a fully automated pipeline for performing reduced-order cardiovascular simulations. MIROS integrates SeqSeg-based geometry generation with reduced order modeling of blood flow and semi-automatic boundary condition assignment to produce hemodynamic simulations within minutes. This approach significantly reduces the computational and manual burden traditionally required, enabling rapid, patient-specific analyses and facilitating largescale studies.Finally, building on SeqSeg and inspired by advances in human trajectory forecasting, we propose VesselTrajNet, a novel method for vasculature tracking in medical images. By adapting a U-Net-based Gaussian heat map encoder-decoder architecture for multiple goal-driven path prediction, VesselTrajNet accurately models complex vascular branching without requiring explicit bifurcation detection. We demonstrate its utility on coronary artery CT data, underscoring its potential for diagnostic and interventional imaging.Together, these contributions advance the state of the art in automated cardiovascular modeling and simulation. By harnessing deep learning for the modeling pipeline, this work aims to make high-fidelity cardiovascular simulations more accessible, scalable, and clinically relevant.
■590 ▼aSchool code: 0028.
■650 4▼aMechanical engineering
■650 4▼aComputer science
■650 4▼aMedical imaging
■653 ▼aCardiovascular modeling
■653 ▼aDeep learning
■653 ▼aHemodynamics simulation
■653 ▼aMedical image analysis
■653 ▼aPatient-specific modeling
■653 ▼aVascular segmentation
■690 ▼a0548
■690 ▼a0984
■690 ▼a0574
■71020▼aUniversity of California, Berkeley▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g87-04B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358556▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
