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https://github.com/cns-iu/hra-sennet-hoa-kaggle-2024
Code for "Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys"
https://github.com/cns-iu/hra-sennet-hoa-kaggle-2024
computer-vision deep-learning kaggle-competition machine-learning publication-code vasculature
Last synced: about 1 month ago
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Code for "Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys"
- Host: GitHub
- URL: https://github.com/cns-iu/hra-sennet-hoa-kaggle-2024
- Owner: cns-iu
- License: mit
- Created: 2024-04-30T20:10:07.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-08-27T18:42:12.000Z (4 months ago)
- Last Synced: 2024-08-28T00:25:41.277Z (4 months ago)
- Topics: computer-vision, deep-learning, kaggle-competition, machine-learning, publication-code, vasculature
- Language: Jupyter Notebook
- Homepage:
- Size: 14.5 MB
- Stars: 0
- Watchers: 6
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Code for "Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys"
Yashvardhan Jain1*+, Claire L. Walsh2*+, Ekin Yagis2, Shahab Aslani2, Sonal Nandanwar2, Yang Zhou2, Juhyung Ha1, Katherine S. Gustilo1, Joseph Brunet2,3, Shahrokh Rahmani2,4, Paul Tafforeau3, Alexandre Bellier5, Griffin Weber6, Peter D. Lee2, Katy Börner1*
1 Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA
2 Department of Mechanical Engineering, University College London, London, UK
3 European Synchrotron Radiation Facility, Grenoble, France
4 National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK
5 Univ. Grenoble Alpes, Department of Anatomy (LADAF), Grenoble, France
6 Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
+These authors contributed equally
*Corresponding authors
Yashvardhan Jain ([email protected])
Claire Walsh ([email protected])
Katy Börner ([email protected])
## Abstract
Efficient algorithms are needed to segment vasculature in new three-dimensional (3D) medical imaging datasets at scale for a wide range of research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive. Computational approaches are more scalable but have limitations in accuracy. We organized a global machine learning competition, engaging 1,401 participants, to help develop new deep learning methods for 3D blood vessel segmentation. This paper presents a detailed analysis of the top-performing solutions using manually curated 3D Hierarchical Phase-Contrast Tomography datasets of the human kidney, focussing on the segmentation accuracy and morphological analysis, thereby establishing a benchmark for future studies in blood vessel segmentation within phase-contrast tomography imaging.
### Link to competition website: https://www.kaggle.com/competitions/blood-vessel-segmentation
Link to Skeleton Analysis files: https://github.com/HiPCTProject/Kaggle_skeleton_analyses