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

Al-Waisy, A.S.
; 
Alruban, A.
; 
Al-Fahdawi, S.
; 
Qahwaji, Rami S.R.
; 
Ponirakis, G.
; 
Malik, R.A.
; 
Mohammed, M.A.
; 
Kadry, S.
Publication Date
2022-02-20
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Keywords
Complex wavelet transform, Deep learning, Convolutional neural network, U-Net architecture, Corneal confocal microscopy, Corneal endothelial cells
Rights
(c) 2022 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (https://creativecommons.org/licenses/by/4.0/)
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Yes
Open Access status
openAccess
Accepted for publication
2022-01-18
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Engineering and Digital Technology Publications
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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
URI
http://hdl.handle.net/10454/18782
Version
Published version
Citation
Al-Waisy AS, Alruban A, Al-Fahdawi S et al (2022) CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells. Mathematics. 10(3): 320.
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https://doi.org/10.3390/math10030320
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Article
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