Science and Research

Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography

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 three kidneys imaged using Hierarchical Phase-Contrast Tomography (HiP-CT) as part of the Human Organ Atlas Project. HiP-CT, pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering resolution around 20

  • Yagis, E.
  • Aslani, S.
  • Jain, Y.
  • Zhou, Y.
  • Rahmani, S.
  • Brunet, J.
  • Bellier, A.
  • Werlein, C.
  • Ackermann, M.
  • Jonigk, D.
  • Tafforeau, P.
  • Lee, P. D.
  • Walsh, C.
Publication details
DOI: 10.21203/rs.3.rs-4613439/v1
Journal: Res Sq
Work Type: Original
Location: BREATH
Disease Area: PLI
Partner / Member: MHH
Access-Number: 39070623

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