Developing a 10-Layer Retinal Segmentation for MacTel Using Semi-Supervised Learning
Improving automated segmentation of MacTel pathology on OCT imaging by leveraging unlabeled data may enable more accurate assessment of disease progression, and this approach may be useful for improving feature identification and location on OCT in other rare diseases as well.
Authors
- Marian Blazes
- Aaron Lee (ORCID: https://orcid.org/0000-0002-7452-1648)
- Martin Friedlander (ORCID: https://orcid.org/0000-0003-4238-9651)
- Yue Wu (ORCID: https://orcid.org/0000-0001-7441-6269)
- Aditi Verma (ORCID: https://orcid.org/0009-0007-3134-0808)
- Simone Tzaridis (ORCID: https://orcid.org/0000-0002-8380-6485)
Institutions
- Scripps Research Institute (US)
- University of Washington (US)
- The Lowy Medical Research Institute (US)
- The Retina Center (US)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1167/tvst.13.11.2
- Citations
- 3
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Research to Prevent Blindness
- Carl Zeiss Meditec AG
- Santen
- National Institutes of Health
- Genentech