Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset
The method outperformed traditional max-flow and a transformer-based method, demonstrating its effectiveness in handling complex fracture.
Authors
- Yudi Sang (ORCID: https://orcid.org/0000-0002-9971-2993)
- Chao Shi (ORCID: https://orcid.org/0000-0002-4544-4639)
- Gang Zhu (ORCID: https://orcid.org/0000-0002-3905-4925)
- Sutuke Yibulayimu (ORCID: https://orcid.org/0000-0001-5115-9255)
- Xinbao Wu (ORCID: https://orcid.org/0000-0003-3136-1688)
- Chendi Liang (ORCID: https://orcid.org/0000-0001-8202-816X)
- Yanzhen Liu (ORCID: https://orcid.org/0000-0003-3766-1171)
- Yu Wang (ORCID: https://orcid.org/0000-0003-0467-6465)
- Chunpeng Zhao (ORCID: https://orcid.org/0000-0003-4284-161X)
Institutions
- Peking University (CN)
- Beijing Jishuitan Hospital (CN)
- Wuhu Hit Robot Technology Research Institute (CN)
- Beihang University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-04-15
- DOI
- https://doi.org/10.3389/fmed.2025.1511487
- Citations
- 5
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 3.37
Funders
- National Key Research and Development Program of China