Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography
The study underscores the efficacy of deep learning models in automating CFZ segmentation and introduces quantitative features as biomarkers for DR. These findings support further exploration of CFZ analysis in retinal disease diagnostics and therapeutic monitoring.
Authors
- Xincheng Yao (ORCID: https://orcid.org/0000-0002-0356-3242)
- Behrouz Ebrahimi (ORCID: https://orcid.org/0000-0003-3390-7330)
- Jennifer I. Lim (ORCID: https://orcid.org/0000-0001-7791-6998)
- Albert Kofi Dadzie (ORCID: https://orcid.org/0000-0002-3466-5188)
- Michael J. Heiferman (ORCID: https://orcid.org/0000-0003-3456-0164)
- Mansour Abtahi (ORCID: https://orcid.org/0000-0001-7463-9470)
- Yi‐Ting Hsieh (ORCID: https://orcid.org/0000-0003-2258-154X)
- Mojtaba Rahimi (ORCID: https://orcid.org/0000-0001-6593-905X)
- Srishti Kolla
Institutions
- University of Illinois Urbana-Champaign (US)
- University of Illinois Chicago (US)
- National Taiwan University Hospital (TW)
Publication Details
- Journal
- Journal of Biomedical Optics
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1117/1.jbo.30.5.056005
- Citations
- 1
- Primary Topic
- Optical Coherence Tomography Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- Research to Prevent Blindness
- National Institutes of Health
- Medical Center, University of Pittsburgh
- National Eye Institute