Enhancing semi‐supervised learning for fine‐grained 3D cerebrovascular segmentation with cross‐consistency and uncertainty estimation
The framework's ability to achieve competitive performance across various metrics showcases its potential for relieving human annotation efforts for accurate cerebrovascular extraction tasks, where its effectiveness in handling unlabeled data can offer significant advantages.
Authors
- Nazik Elsayed
- Yousuf Babiker M. Osman (ORCID: https://orcid.org/0000-0001-9612-8866)
- Alou Diakite (ORCID: https://orcid.org/0000-0002-6068-9968)
- Shuqiang Wang (ORCID: https://orcid.org/0000-0003-1119-320X)
- Shanshan Wang (ORCID: https://orcid.org/0000-0002-3824-687X)
- Cheng Li (ORCID: https://orcid.org/0000-0002-3915-5451)
Institutions
- Chinese Academy of Sciences (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1002/mp.70017
- Citations
- 3
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.83