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

Abstract

PurposeTo 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.MethodsA 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.ResultsThe 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).ConclusionsThe 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 RelevanceThe 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.