Automated Image Segmentation of the Corneal Endothelium in Patients With Fuchs Dystrophy

PURPOSE: To perform segmentation of specular microscopy (SM) images of the corneal endothelium for comparing average perimeter length (APL) between Fuchs endothelial corneal dystrophy (FECD) patients and healthy subjects. METHODS: A retrospective review of clinical records of FECD patients and those with healthy endothelium was carried out to collect images of the endothelium. The images were segmented by modified U-Net, a deep learning architecture, followed by the Watershed algorithm to resolve merged cell borders (<5%). The segmented images were analyzed for endothelial cell density (ECD(UW)) and APL. RESULTS: The combination of the U-Net and Watershed algorithm, referred to as the UW approach, enabled a complete segmentation of the endothelium. In healthy, ECD(UW) was close to estimates by SM and manual segmentation (31 subjects; P > 0.1). However, in FECD, ECD(UW) was closer to estimates by manual segmentation but not by SM (27 patients; P < 0.001). ECD(UW) in FECD (2547 ± 499 cells/mm(2); 60 patients) was smaller compared to that in the healthy (2713 ± 401 cells/mm(2); 70 subjects) (P < 0.001). APL in the healthy was 66.87 ± 7.68 µm/cell (70 subjects), but it increased with %Guttae in FECD (56.60–195.30 µm/cell; 60 patients) (P < 0.0001). CONCLUSIONS: The UW approach is precise for the segmentation of SM images from the healthy and FECD. Our analysis has revealed that APL increases with %Guttae. TRANSLATIONAL RELEVANCE: The average perimeter length of the corneal endothelium, which represents the length of the paracellular pathway for fluid flux into the stroma, is increased in Fuchs dystrophy.