Semantic‐consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images
The results suggest that the proposed SCDM enables effective TBI lesion detection and segmentation across diverse TBI CT scans. It significantly reduces false positives by addressing inconsistencies in normal tissue during the iterative image restoration process and mitigating mass effect-induced tissue deformations.
Authors
- Yuru Pei (ORCID: https://orcid.org/0000-0001-8520-3509)
- Leslie Ying (ORCID: https://orcid.org/0000-0001-9801-1362)
- Diya Sun (ORCID: https://orcid.org/0000-0001-5253-4571)
- Tianbing Wang (ORCID: https://orcid.org/0000-0002-5431-0931)
Institutions
- Peking University (CN)
- Beijing University of Technology (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Peking University People's Hospital (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-04-09
- DOI
- https://doi.org/10.1002/mp.17811
- Citations
- 3
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Beijing Municipality