Deep learning-based no-reference quality assessment of anterior segment ultrasound biomicroscopy panoramic images
The DL-based no-reference quality assessment method proposed in this study provides an objective evaluation of anterior segment UBM image quality. It effectively identifies high-quality images, significantly improving the efficiency of ophthalmic imaging professionals and demonstrating strong clinical potential for widespread adoption.
Authors
- Sheng Zhou (ORCID: https://orcid.org/0000-0003-3578-7926)
- Xiaoning Wang (ORCID: https://orcid.org/0000-0001-5608-8812)
- Xinqi Yu (ORCID: https://orcid.org/0000-0003-2029-0124)
- Zhiyuan Zhao (ORCID: https://orcid.org/0009-0006-2282-3982)
- Bin Wu (ORCID: https://orcid.org/0000-0001-5546-3190)
- Qiang Miao (ORCID: https://orcid.org/0000-0002-8879-7266)
- Jiaqi Yang (ORCID: https://orcid.org/0000-0001-6642-0567)
- You Zhou (ORCID: https://orcid.org/0000-0001-9474-9087)
- Xiaochun Wang (ORCID: https://orcid.org/0009-0007-5666-6011)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Tianjin Medical University Eye Hospital (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-10-21
- DOI
- https://doi.org/10.21037/qims-2025-592
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00