Deep learning for robust segmentation of corneal endothelium images in the presence of cornea guttata

Abstract

Automated cell counting in in-vivo specular microscopy images is challenging, especially when single-cell segmentation methods fail due to corneal dystrophy. We aim to obtain reliable cell segmentation from specular microscopy images of both healthy and pathological corneas. Here, we cast the problem of cell segmentation as a supervised multi-class classification problem. Hence, the goal is to learn a mapping relation between an input specular microscopy image and its labeled counterpart, identifying healthy (cells) and dysfunctional regions (e.g., guttae). Using a generative adversarial approach, we trained a U-net model by extracting 96×96 pixel patches from corneal endothelial cell images and the corresponding manual segmentation by a group of expert physicians. Preliminary results show the method's potential to deliver reliable feature segmentation, enabling more accurate cell density estimations for assessing the cornea's state.