Evaluation of Loss Functions for Segmentation of Corneal Nerves

Abstract

Corneal confocal microscopy (CCM) has been advocated as a non-invasive technique for objective diagnosis of very early neuropathy in patients by scanning the corneal subbasal nerve plexus. The obtained images provide a range of research opportunities to be explored. Current research revolves around providing automated solutions for nerve segmentation in CCM images. In this paper, we address the problem of low sensitivity of nerves in automatic segmentation caused by imbalanced pixel distribution in the CCM images. We evaluate three loss functions with varying parameters in the deep learning network, U-Net, on the images and discuss the results. We have observed that the optimal training time and convergence time for the Tversky loss function is better than the binary cross entropy and dice loss functions. This helps in getting better results faster, implying a quicker diagnosis.