Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging

TL;DR

This paper presents a novel automated method for segmenting cone photoreceptors in multimodal adaptive optics images, improving accuracy for retinal disease diagnosis.

Contribution

It introduces a dual-modality segmentation approach using U-Net-based models, enhancing reliability and clinical applicability in retinal imaging.

Findings

Consistent segmentation results across confocal and non-confocal images.

Improved accuracy in cone cell shape, area, and distribution analysis.

Potential for better retinal health assessment.

Abstract

Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors for improved accuracy. Precise segmentation is crucial for understanding each cone cell's shape, area, and distribution. It helps to estimate the surrounding areas occupied by rods, which allows the calculation of the density of cone photoreceptors in the area of interest. In turn, density is critical for evaluating overall retinal health and functionality. We explored two U-Net-based segmentation models: StarDist for confocal and Cellpose for calculated modalities. Analyzing cone cells in images from two modalities and achieving consistent results demonstrates the study's reliability and potential for clinical application.