Comparing Interactive Machine Learning and Unassisted Segmentation of Geographic Atrophy From Fundus Autofluorescence
OPTIMEyes can be integrated in clinician's workflows to provide automated segmentations that can be easily manually improved, increasing the task's effectiveness and efficiency. In addition, it enables the efficient collection of high-quality manually labeled large imaging datasets that can be of great interest to the research community.
Authors
- Barry A. Marks
- Kathryn Abe-Ridgway
- Benjamin Bearce
- Andres Lisker
- Dallin Milner
- Praveer Singh (ORCID: https://orcid.org/0000-0001-6641-2030)
- Matthew Ryan Tukel
- Giacomo Nebbia (ORCID: https://orcid.org/0000-0002-4766-6278)
- Scott Kinder
- Ramya Gnanaraj
- Jayashree Kalpathy-Cramer
- Steve McNamara
- Isabelle Roseto
- Luis Barrientos
- Zafar Gill
Institutions
- University of Colorado Anschutz Medical Campus (US)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2026-01-08
- DOI
- https://doi.org/10.1167/tvst.15.1.12
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Research to Prevent Blindness
- Regeneron Pharmaceuticals
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health
- Genentech
- National Center for Advancing Translational Sciences