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

Alaa S. Al-Waisy, Abdulrahman Alruban, Shumoos Al-Fahdawi, Rami Qahwaji, Georgios Ponirakis, Rayaz A. Malik, Mazin Abed Mohammed and Seifedine Kadry
Additional contact information
Alaa S. Al-Waisy: Computer Technologies Engineering Department, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad 10064, Iraq
Abdulrahman Alruban: Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia
Shumoos Al-Fahdawi: Computer Science Department, Al-Ma’aref University College, Ramadi, Anbar 31001, Iraq
Rami Qahwaji: School of Electrical Engineering and Computer Science, University of Bradford, Bradford BD7 1DP, UK
Georgios Ponirakis: Division of Medicine, Weill Cornell Medicine-Qatar, Doha 24144, Qatar
Rayaz A. Malik: Division of Medicine, Weill Cornell Medicine-Qatar, Doha 24144, Qatar
Mazin Abed Mohammed: Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq
Seifedine Kadry: Faculty of Applied Computing and Technology, Noroff University College, 4612 Kristiansand, Norway

Mathematics, 2022, vol. 10, issue 3, 1-26

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/mm 2 ), mean cell area (MCA, μm 2 ), 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.

Keywords: corneal confocal microscopy; corneal endothelial cells; Complex Wavelet Transform; deep learning; Convolutional Neural Network; U-Net architecture (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/3/320/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/3/320/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:3:p:320-:d:729504

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().