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

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    al-waisy_et_al_2022 (16.60Mb)
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    Publication date
    2022-02-20
    Author
    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 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/)
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
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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.
    Link to publisher’s version
    https://doi.org/10.3390/math10030320
    Type
    Article
    Collections
    Engineering and Informatics Publications

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