Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.

ABSTRACT: Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from t

SUBMITTER: Yagis E 

PROVIDER: S-EPMC11549215 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature