Generating density maps for convolutional neural network-based cell counting in specular microscopy images

Abstract

Accurate endothelial cell density with specular microscopy is essential for correct clinical assessment of the cornea. Commercial specular microscopes incorporate automated cell segmentation methods to estimate cell density. However, these methods are prone to false cell detections in pathological corneas. This project aims to obtain a reliable automated cell density from specular microscopy images of both healthy and pathological corneas with convolutional neural networks. Convolutional neural networks require labeled datasets. Thus, we developed custom software for producing a curated dataset of labeled ground-truth images and cell density maps. In this paper, we implemented a fully convolutional regression network to predict the cell density map from the input microscopy image. Encouraging preliminary results show the potential of the method. This approach may pave the way for dealing with the variability of corneal endothelial cell images.