St-RegSeg: an unsupervised registration-based framework for multimodal magnetic resonance imaging stroke lesion segmentation
The St-RegSeg framework provides a highly effective solution for the registration and segmentation of multimodal MRI images in ischemic stroke cases. Its performance metrics and computational efficiency significantly outperform existing methods, making it a promising tool for clinical applications. The code is open-sourced and available at: https://github.com/Cooper-Gu/St-RegSeg.
Authors
- Dong Ming (ORCID: https://orcid.org/0000-0002-8192-2538)
- Xingwei An (ORCID: https://orcid.org/0000-0002-5417-508X)
- Chengzhi Gui (ORCID: https://orcid.org/0009-0005-7117-0036)
- Shuang Liu (ORCID: https://orcid.org/0000-0001-8766-7235)
- Tingting Li (ORCID: https://orcid.org/0009-0007-5676-069X)
Institutions
- Tianjin University (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-01
- DOI
- https://doi.org/10.21037/qims-24-725
- Citations
- 4
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.99
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Tianjin City