Generalist Segmentation Algorithm for Photoreceptors Analysis in Adaptive Optics Imaging

TL;DR

This paper presents a deep learning-based segmentation method for cone photoreceptors in adaptive optics retinal images, achieving high accuracy with minimal labeled data, aiding early eye condition detection.

Contribution

Introduces a semi-automated deep learning approach for cone segmentation in AOSLO images that reduces the need for extensive labeled datasets.

Findings

F1 scores above 0.95 across all tested eccentricities

Outperforms previous deep learning methods in cone segmentation accuracy

Requires only a fraction of labeled data, easing data annotation challenges

Abstract

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labelling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labelled dataset of 20 AOSLO batches of images of 18 participants for 0$^{\circ}$, 1$^{\circ}$, and 2$^{\circ}$ from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0$^{\circ}$, 1$^{\circ}$, and 2$^{\circ}$, respectively, which is better than previously reported DL approaches. Our method minimizes the need for labelled data by only necessitating a fraction of labelled cones, which is especially beneficial in the field of ophthalmology, where labelled data can often be limited.