Automatic Determination of Endothelial Cell Density From Donor Cornea Endothelial Cell Images

Abstract

Purpose: To determine endothelial cell density (ECD) from real-world donor cornea endothelial cell (EC) images using a self-supervised deep learning segmentation model.

Methods: Two eye banks (Eversight, VisionGift) provided 15,138 single, unique EC images from 8169 donors along with their demographics, tissue characteristics, and ECD. This dataset was utilized for self-supervised training and deep learning inference. The Cornea Image Analysis Reading Center (CIARC) provided a second dataset of 174 donor EC images based on image and tissue quality. These images were used to train a supervised deep learning cell border segmentation model. Evaluation between manual and automated determination of ECD was restricted to the 1939 test EC images with at least 100 cells counted by both methods.

Results: The ECD measurements from both methods were in excellent agreement with rc of 0.77 (95% confidence interval [CI], 0.75-0.79; P < 0.001) and bias of 123 cells/mm2 (95% CI, 114-131; P < 0.001); 81% of the automated ECD values were within 10% of the manual ECD values. When the analysis was further restricted to the cropped image, the rc was 0.88 (95% CI, 0.87-0.89; P < 0.001), bias was 46 cells/mm2 (95% CI, 39-53; P < 0.001), and 93% of the automated ECD values were within 10% of the manual ECD values.

Conclusions: Deep learning analysis provides accurate ECDs of donor images, potentially reducing analysis time and training requirements.

Translational relevance: The approach of this study, a robust methodology for automatically evaluating donor cornea EC images, could expand the quantitative determination of endothelial health beyond ECD.

Figure 1.

Training workflow for deep learning–based endothelial cell segmentation. Self-supervised training was performed using 12,114 images through a ViT to learn feature representations without labeled data. Learned weights were then used to initialize a UNETR for supervised cell border segmentation training with 174 images and labels. Final output after postprocessing was a single-pixel-width cell border segmentation from which ECD was automatically calculated. The illustrations of the ViT and UNETR are based on work by Dosovitskiy et al. and Hatamizadeh et al., respectively.

Figure 2.

Example of donor cornea endothelial cell images and comparative display of corresponding manual and automatic annotations overlay. Top row: (1a) and (2a) are specular microscopic images from donor cornea endothelium provided by Eversight and VisionGift, respectively. Bottom row: (1b) and (2b) depict automated cell border annotations (green) and manual cell centroid annotations (red, blue, and purple).

Figure 3.

(A) Bland–Altman plot of whole image manual versus automated ECD (N = 1939). Bland–Altman plot of whole image ECDs (cells/mm²) determined by study eye banks versus the ECDs determined by the deep learning model. The difference in ECDs is plotted against their average value. The Bland–Altman plot shows a small, estimated bias (mean difference) of 123 (95% CI, 114–131), with a lower limit of agreement (LoA) of −234 (95% CI, −248 to −220) and an upper limit of agreement of 479 (95% CI, 462–497). Auto, automated. (B) Bland–Altman plot of cropped images of manual versus automated ECDs (N = 1939). Bland–Altman plot of cropped image ECDs (cells/mm²) was determined by study eye banks versus the ECDs determined by the deep learning model. The difference in ECDs is plotted against their average value. The Bland–Altman plot shows a small, estimated bias (mean difference) of 46 (95% CI, 39–53), with a lower limit of agreement of −243 (95% CI, −258 to −228) and an upper limit of agreement of 335 (95% CI, 319–352).