Journal article
A Pilot Study of Deep Learning-Based Monocular Depth Estimation from Fundus Photographs
Medinformatics, Vol.2(4), pp.279-287
2025
DOI: 10.47852/bonviewMEDIN42023933
Abstract
The purpose of this study was to evaluate the feasibility of a generalizable deep-learning (DL) based system with no a priori knowledge of fundus photographs to generate monocular depth map information about optic disc structures from this imaging modality. Images of 30 stereo pairs of fundus photographs centered on the optic disc of 30 subjects were analyzed with this DL system to generate monocular depth maps using zero-shot cross-dataset transfer. These maps were registered onto reference standard depth maps derived from Optical Coherence Tomography. Accuracy of the DL system was assessed by the root of mean squared error (RMSE) between the estimate and reference standard. 47% of the total images from the dataset were successfully processed, with mean RMSE of 0.081. Our findings demonstrate that single image, monocular depth estimation with a generalizable DL system using zero-shot cross-dataset transfer applied to retinal color fundus photographs is feasible and has potential.
Details
- Title: Subtitle
- A Pilot Study of Deep Learning-Based Monocular Depth Estimation from Fundus Photographs
- Creators
- Rony Gelman - New York UniversityMichael D. Abràmoff - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Medinformatics, Vol.2(4), pp.279-287
- DOI
- 10.47852/bonviewMEDIN42023933
- ISSN
- 3029-1321
- eISSN
- 3029-1321
- Language
- English
- Electronic publication date
- 10/21/2024
- Date published
- 2025
- Academic Unit
- Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984737132402771
Metrics
46 Record Views