Automatic segmentation and volume measurement of anterior visual pathway in brain 3D-T1WI using deep learning
We provide normative values for the automatic MRI measurement of the AVP in adults. The 3D UX-Net model based on brain T1WI achieves high accuracy in segmenting and measuring the volume of the AVP.
Authors
- Haixiang Wang (ORCID: https://orcid.org/0000-0002-5040-6055)
- Wenlong Lin (ORCID: https://orcid.org/0000-0002-8763-7893)
- Linquan Dai
- Qiyuan Zhu (ORCID: https://orcid.org/0009-0009-3977-8710)
- Yiqiu Wei (ORCID: https://orcid.org/0000-0003-1556-7432)
- Jingjie Wang (ORCID: https://orcid.org/0000-0003-3700-3013)
- Yongmei Li
- Yongliang Han (ORCID: https://orcid.org/0009-0003-1531-7123)
- Qi Luo (ORCID: https://orcid.org/0000-0002-0393-6352)
- Qiao Zheng (ORCID: https://orcid.org/0000-0002-5713-3870)
- Chun Zeng (ORCID: https://orcid.org/0000-0002-5682-1452)
- Shaoguo Cui (ORCID: https://orcid.org/0009-0007-7703-9236)
Institutions
- Chongqing Normal University (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-04-28
- DOI
- https://doi.org/10.3389/fmed.2025.1530361
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chongqing Medical University
- Natural Science Foundation of Chongqing
- Centre Scientifique et Technique du Bâtiment