Modified CNN-Watershed for Corneal Endothelium Segmentation: Image-to-Image Versus Sliding-Window Comparison

This paper considers the problem of corneal endothelium image segmentation using a method that combines a CNN model with a watershed transform. Specifically, first CNN predicts cell bodies, edges, and centers. Next, cell centers are used as markers that guide the watershed transform performed concerning the cell edge probability maps inferred by the CNN to outline cell edges. Different variants of the method are considered. Specifically, a downscaled U-Net is compared with the Attention U-Net in the image-to-image and sliding window setup. Results show that using a marker-driven watershed transform to post-process cell edge probability maps allows for replacing the sliding window setup with an image-to-image setup, reducing prediction time while maintaining similar or better segmentation accuracy. Also, when used as a backbone, Attention U-Net outperforms classical U-Net in determining cell morphometric parameters with high accuracy.