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All Journal Journal of Healthcare and Biomedical Science (JHBS)
Muhammad Fauzi Daud
Institute of Medical Science Technology; Universiti Kuala Lumpur (UniKL)
Biochemistry, Genetics & Molecular Biology Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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A Data-Centric Approach to HEK Cell Microscopic Image Segmentation using Multi-Scaling U-Net Syiham Fakhrulradzi Abdul Aziz; Ahmad Syahrin Idris; Siti Fauziah Toha; Izyan Mohd Idris; Muhammad Fauzi Daud; Azam Ahmad Bakir; Low Siow Yong
Journal of Healthcare and Biomedical Science Vol. 4 No. 2 (2026): Journal of Healthcare and Biomedical Science (JHBS)
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/jhbs.v4i2.4402

Abstract

Accurate and reproducible cell culture monitoring is important in biomedical research and regenerative medicine, yet manual assessment of cell confluency and morphology remains subjective and prone to inter-observer variability. Although deep learning approaches have been widely applied to cell segmentation, their systematic application to Human Embryonic Kidney (HEK) cells using data-centric methodologies remains underexplored. This study addresses this gap by implementing a Multi-Scaling U-Net (MSUNet) architecture combined with a data-centric workflow that emphasizes improving data quality through Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing, Total Variation Denoising (TVD), and iterative expert-guided annotation refinement. The scope of analysis was limited to HEK293T cell images captured at 10x magnification using phase-contrast microscopy. The optimized model achieved an Intersection over Union (IoU) of 0.8980 after applying the data-centric approach, representing a 15.1% relative improvement over the baseline model trained without preprocessing or annotation refinement. These findings provide empirical evidence that systematic data quality improvement constitutes a key contributing factor to segmentation performance, offering a reproducible methodology for automated cell confluency measurement in resource-constrained laboratory settings.
Co-Authors Ahmad Syahrin Idris Azam Ahmad Bakir Izyan Mohd Idris Low Siow Yong Siti Fauziah Toha Syiham Fakhrulradzi Abdul Aziz