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

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...

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Bibliographic Details
Main Authors: Ekin Yagis, Shahab Aslani, Yashvardhan Jain, Yang Zhou, Shahrokh Rahmani, Joseph Brunet, Alexandre Bellier, Christopher Werlein, Maximilian Ackermann, Danny Jonigk, Paul Tafforeau, Peter D. Lee, Claire L. Walsh
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Deep learning
X-ray tomography
Semantic segmentation
3D vascular segmentation
Online Access:https://doi.org/10.1038/s41598-024-77582-5

Internet

https://doi.org/10.1038/s41598-024-77582-5

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