A Pilot Study of Deep Learning-Based Monocular Depth Estimation from Fundus Photographs

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.

Received: 24 July 2024 | Revised: 30 September 2024 | Accepted: 10 October 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in Iowa Carver College of Medicine at https://medicine.uiowa.edu/eye/inspire-datasets, reference number [15]; in Github at https://github.com/isl-org/MiDaS, reference number [18]; in PyTorch at https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html, reference number [19]; in ImageJ Docs at https://imagej.net/software/imagej2/, reference number [23].

Author Contribution Statement

Rony Gelman: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Michael D. Abràmoff: Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.