Mobile-CellNet: Automatic Segmentation of Corneal Endothelium Using an Efficient Hybrid Deep Learning Model.

ABSTRACT:

Purpose

The corneal endothelium, the innermost layer of the human cornea, exhibits a morphology of predominantly hexagonal cells. These endothelial cells are believed to have limited regeneration capacity, and their density decreases over time. Endothelial cell density (ECD) can therefore be used to measure the health of the corneal endothelium and the overall cornea. In clinical settings, specular microscopes are used to image this layer. Owing to the unavailability of reliable automatic tools, technicians often manually mark the cell centers and borders to measure ECD for such images, a process that is time and resource-consuming.

Methods

In this article, we propose Mobile-CellNet, a novel completely automatic, efficient deep learning-based cell segmentation algorithm to e

SUBMITTER: Karmakar R 

PROVIDER: S-EPMC9992284 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature