Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects.

Valterova, Eva; Unterlauft, Jan D; Francke, Mike; Kirsten, Toralf; Kolar, Radim; Rauscher, Franziska G (2023). Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects. Biomedical optics express, 14(2), pp. 945-970. Optical Society of America 10.1364/BOE.471881

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This work presents a novel fully automated method for retinal analysis in images acquired with a flood illuminated adaptive optics retinal camera (AO-FIO). The proposed processing pipeline consists of several steps: First, we register single AO-FIO images in a montage image capturing a larger retinal area. The registration is performed by combination of phase correlation and the scale-invariant feature transform method. A set of 200 AO-FIO images from 10 healthy subjects (10 images from left eye and 10 images from right eye) is processed into 20 montage images and mutually aligned according to the automatically detected fovea center. As a second step, the photoreceptors in the montage images are detected using a method based on regional maxima localization, where the detector parameters were determined with Bayesian optimization according to manually labeled photoreceptors by three evaluators. The detection assessment, based on Dice coefficient, ranges from 0.72 to 0.8. In the next step, the corresponding density maps are generated for each of the montage images. As a final step, representative averaged photoreceptor density maps are created for the left and right eye and thus enabling comprehensive analysis across the montage images and a straightforward comparison with available histological data and other published studies. Our proposed method and software thus enable us to generate AO-based photoreceptor density maps for all measured locations fully automatically, and thus it is suitable for large studies, as those are in pressing need for automated approaches. In addition, the application MATADOR (MATlab ADaptive Optics Retinal Image Analysis) that implements the described pipeline and the dataset with photoreceptor labels are made publicly available.

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