Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography

The foveola, the central region of the human retina, plays a crucial role in sharp color vision and is challenging to study due to its unique anatomy and technical limitations in imaging. We present {ConeMapper}, an open-source {MATLAB} software that integrates a fully convolutional neural network ({FCN}) for the automatic detection and analysis of cone photoreceptors in confocal adaptive optics scanning light ophthalmoscopy ({AOSLO}) images of the foveal center. The {FCN} was trained on a dataset of 49 healthy retinas and showed improved performance over previously published neural networks, particularly in the central fovea, achieving an \$\$F\_1\$\$score of 0.9769 across the validation set, critically reducing analysis time. In addition to automatic cone detection, {ConeMapper} provides efficient manual annotation tools, visualizations and topographical analysis, offering users detailed metrics for further analysis. {ConeMapper} is freely available, with ongoing development aimed at enhancing functionality and adaptability to different retinal imaging modalities.