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

Abstract

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 (ECDUW) 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, ECDUW was close to estimates by SM and manual segmentation (31 subjects; P > 0.1). However, in FECD, ECDUW was closer to estimates by manual segmentation but not by SM (27 patients; P < 0.001). ECDUW in FECD (2547 ± 499 cells/mm2; 60 patients) was smaller compared to that in the healthy (2713 ± 401 cells/mm2; 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.