Automated Assessment of Photoreceptor Visibility in Adaptive Optics Split-Detection Images Using Edge Detection

Adaptive optics scanning laser ophthalmoscopy (AOSLO) is a high-resolution imaging modality that allows measurements of cellular-level retinal changes in living patients. In retinal diseases, the visibility of photoreceptors in AOSLO images is affected by pathology, patient motion, and optics, which can lead to variability in analyses of the photoreceptor mosaic. Current best practice for AOSLO mosaic quantification requires manual assessment of photoreceptor visibility across overlapping images, a laborious and time-consuming task.

We propose an automated measure for quantification of photoreceptor visibility in AOSLO. Our method detects salient edge features, which can represent visible photoreceptor boundaries in each image. We evaluate our measure against two human graders and two standard automated image quality assessment algorithms.

We evaluate the accuracy of pairwise ordering (PO) and the correlation of ordinal rankings (ORs) of photoreceptor visibility in 29 retinal regions, taken from five subjects with choroideremia. The proposed measure had high association with manual assessments (Grader 1: PO = 0.71, OR = 0.61; Grader 2: PO = 0.67, OR = 0.62), which is comparable with intergrader reliability (PO = 0.76, OR = 0.75) and outperforms the top standard approach (PO = 0.57; OR = 0.46).

Our edge-based measure can automatically assess photoreceptor visibility and order overlapping images within AOSLO montages. This can significantly reduce the manual labor required to generate high-quality AOSLO montages and enables higher throughput for quantitative studies of photoreceptors.

Automated assessment of photoreceptor visibility allows us to more rapidly quantify photoreceptor morphology in the living eye. This has applications to ophthalmic medicine by allowing detailed characterization of retinal degenerations, thus yielding potential biomarkers of treatment safety and efficacy.