Manual and Automatic Segmentations of the Fibrillar Layer in Fuchs Endothelial Corneal Dystrophy Eyes

Abstract

Purpose : Subendothelial collagen deposits, referred to as fibrillar layer (FL), are present in about 80% of eyes with advanced Fuchs endothelial corneal dystrophy (FECD). We investigated reliability and reproducibility of FL segmentations on Scheimpflug corneal densitometry images and trained a deep neural network for automatic segmentation.

Methods : In a retrospective monocenter study, high-quality preoperative Scheimpflug images of patients undergoing DMEK or triple DMEK for advanced FECD were included. FL-negative cases were excluded. Images were manually segmented by two graders with the open-source software MITK. A MATLAB-based code was generated for quantitative and spatial analysis calculated the region of interest (ROI), maximum caliper diameter, horizontal and vertical diameter, and Dice similarity coefficient for FL-areas. The FL localization was visualized by spatial heatmaps. The intraclass correlation coefficient (ICC) was calculated by IBM SPSS. The segmentations of Grader 1 and 2 were combined and used as ground truth for training a neural network (U-Net) for FL segmentation. 25 images were reserved as test set. Of the remaining data, 176 (80%) images were used for training, 44 (20 %) for validation. Accuracy metrics (Dice and Jaccard Coefficient) were calculated on the test set to quantify the degree of similarity between the manual segmentations (ground truth) and the model.

Results : Among 308 eyes, 248 were FL-positive (80,5%) and 60 FL-negative (19,5%). Intra-observer Grader 1 vs intra-observer Grader 2 vs inter-observer results include: ROI 7.91mm² ±4.8 vs 9.29mm²±5.0, ICC (ROI) at 0.957 vs 0.930 vs 0.954, and Dice similarity coefficient 0.86 vs 0.89 vs 0.85. Spatial heatmaps demonstrated inferotemporal localization of increased densitometry areas in FL-positive eyes. The model was trained over 300 epochs. High accuracy in Dice (0.92) and Jaccard Coefficent (0.85) was achieved after 150 epochs on the test set.

Conclusions : The FL can be reliably and reproducibly visualized on corneal densitometry images in the clinical routine and projects to the inferotemporal corneal quadrant. A neural network can be trained for automatic segmentation that achieves comparable or even better results than manual segmentation. In the future, the FL could play a role in the individualization of descemetorhexis in DMEK and in the allocation of rare donor tissue.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.