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 (Research Synergy Foundation, 2026)
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.