Eye-Opening Advances: Automated 3D Segmentation, Key Biomarkers Extraction, and the First Large-Scale MRI Eye Atlas.

This study addresses the need for an automated and accurate 3D segmentation of the healthy human eye and orbit from Magnetic Resonance Images, to allow improved ophthalmic diagnostics and treatments. Past efforts primarily focused on small sample sizes and varied imaging modalities. Here, we leverage a large-scale dataset of T1-weighted MRI of 1245 subjects and the use of the deep learning-based nnU-Net for MR-Eye segmentation tasks. The results showcase robust and accurate segmentations of lens, globe, optic nerve, rectus muscles, and fat. We also present the automated estimation of key ophthalmic biomarkers such as AL and volumetry, while benchmarking correlations between body mass index (BMI) and eye structure volumes. Quality control protocols are introduced through the pipeline to ensure the reliability and clinical relevance of the segmented large-scale data, further enhancing the applicability of our algorithm.