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

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.

In this article, we propose Mobile-CellNet, a novel completely automatic, efficient deep learning-based cell segmentation algorithm to estimate ECD. This uses 2 similar image segmentation models working in parallel along with image postprocessing using classical image processing techniques. We also compare the proposed algorithm with widely used biomedical image segmentation networks U-Net and U-Net++.

The proposed technique achieved a mean absolute error of 4.06% for the ECD on the test set, comparable with the error for U-Net of 3.80% ( P = 0.185 for difference), but requiring almost 31 times fewer floating-point operations (FLOPs) and 34 times fewer parameters.

Mobile-CellNet accurately segments corneal endothelial cells and reports ECD and cell morphology efficiently. This can be used to develop tools to analyze specular corneal endothelial images in remote settings.