Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging
This work addresses the need for precise cone cell analysis to diagnose and manage retinal diseases, though it is incremental as it applies existing segmentation methods to new imaging modalities.
The study tackled the problem of accurately detecting and segmenting cone photoreceptors in retinal images using multimodal adaptive optics imaging, achieving consistent results with U-Net-based models like StarDist and Cellpose for potential clinical use.
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.