Deep-learning-based segmentation of perivascular spaces on T2-Weighted 3T magnetic resonance images
The proposed method can accurately segment PVSs; it will facilitate practical clinical applications and is expected to replace the method of visual counting directly on T1-weighted images or T2-weighted images.
Authors
- Jiaying Lin (ORCID: https://orcid.org/0000-0002-1473-5636)
- Xinting Ge
- Minmin Pan (ORCID: https://orcid.org/0000-0001-5366-8387)
- Mengting Liu
- Jun Xia (ORCID: https://orcid.org/0000-0002-9772-9951)
- Wenjie He (ORCID: https://orcid.org/0000-0002-7840-4625)
- Die Cai
- Chenyuan Liu
- Rui Li (ORCID: https://orcid.org/0009-0009-8136-4159)
Institutions
- Central South University (CN)
- Sun Yat-sen University (CN)
- Shandong Normal University (CN)
- Shenzhen Second People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2024-08-29
- DOI
- https://doi.org/10.3389/fnagi.2024.1457405
- Citations
- 3
- Primary Topic
- Cerebrospinal fluid and hydrocephalus
- Type
- article
- Field-Weighted Citation Impact
- 0.80