CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells

Alaa S Al-Waisy,Georgios Ponirakis,Rami Qahwaji,Shumoos Al-Fahdawi,Seifedine Kadry,Abdulrahman Alruban,Rayaz A Malik,Mazin Abed Mohammed

doi:10.3390/math10030320

Abstract

The quantification of corneal endothelial cell (CEC) morphology using manual and semi-automatic software enables an objective assessment of corneal endothelial pathology. However, the procedure is tedious, subjective, and not widely applied in clinical practice. We have developed the CellsDeepNet system to automatically segment and analyse the CEC morphology. The CellsDeepNet system uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of the CEC images and reduce the effects of non-uniform image illumination, 2D Double-Density Dual-Tree Complex Wavelet Transform (2DDD-TCWT) to reduce noise, Butterworth Bandpass filter to enhance the CEC edges, and moving average filter to adjust for brightness level. An improved version of U-Net was used to detect the boundaries of the CECs, regardless of the CEC size. CEC morphology was measured as mean cell density (MCD, cell/mm2), mean cell area (MCA, μm2), mean cell perimeter (MCP, μm), polymegathism (coefficient of CEC size variation), and pleomorphism (percentage of hexagonality coefficient). The CellsDeepNet system correlated highly significantly with the manual estimations for MCD (r = 0.94), MCA (r = 0.99), MCP (r = 0.99), polymegathism (r = 0.92), and pleomorphism (r = 0.86), with p < 0.0001 for all the extracted clinical features. The Bland–Altman plots showed excellent agreement. The percentage difference between the manual and automated estimations was superior for the CellsDeepNet system compared to the CEAS system and other state-of-the-art CEC segmentation systems on three large and challenging corneal endothelium image datasets captured using two different ophthalmic devices.

Highlights

In Vivo Confocal Microscopy (IVCM) is a rapid non-invasive imaging method used to capture high-resolution images from all corneal layers [1]
Using the Endothelial Cell Alizarine (ECA) dataset derived from a specular microscope, which is most commonly used in most ophthalmology clinics, the mean differences between manual and automated estimates were less than 1%, 1%, 2.5%, 0.5%, and 0.5% for Mean Cell Density (MCD), Mean Cell Area (MCA), Mean Cell Perimeter (MCP), polymegathism, and pleomorphism, respectively, with no clinical feature with a proportional difference
We have developed the CellsDeepNet system, an improved, fully automated, and fast corneal endothelial cell analysis system

Summary

Introduction

In Vivo Confocal Microscopy (IVCM) is a rapid non-invasive imaging method used to capture high-resolution images from all corneal layers [1]. The images acquired are useful to extract important clinical information and quantify morphological alterations in the human cornea to provide insights into a wide range of corneal endothelial cells’ pathologies and infections. The corneal endothelium is a monolayer of hexagonal corneal endothelial cells (CECs) that line the posterior surface of the cornea [2]. These CECs are vital for corneal transparency by maintaining an optimal state of corneal stromal hydration [3,4,5]. The corneal endothelial layer is comprised of 2300 to 2500 (cells/mm2 ), which are fixed-sized with uniform hexagonal forms and a honeycomb appearance [6]. CEC density and morphology can be used to define the functional ability of donor cornea before corneal transplantation [7,8]

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