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

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Publication date
2022-02-20Author
Al-Waisy, A.S.Alruban, A.
Al-Fahdawi, S.
Qahwaji, Rami S.R.
Ponirakis, G.
Malik, R.A.
Mohammed, M.A.
Kadry, S.
Keyword
Complex wavelet transformDeep 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/)Peer-Reviewed
YesOpen Access status
openAccessAccepted for publication
2022-01-18
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Show full item recordAbstract
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 pVersion
Published versionCitation
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.Link to Version of Record
https://doi.org/10.3390/math10030320Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.3390/math10030320