This study investigates deep-learning methods for segmenting the migration of human hepatocellular carcinoma (HCC) cells in wound-healing assay images. We implemented three state-of-the-art architectures: U-Net, Attention U-Net and U-Net++, and benchmarked them with Dice score and Intersection-over-Union (IoU). Using a proprietary dataset of 177 expertly segmented images, we also performed statistical tests to quantify performance variability. Although no model out-performed the others with statistical significance, Attention U-Net achieved the highest mean scores and exhibited the most normally distributed results. For clinicians and cellular-migration analysts, these findings underscore the added value of attention mechanisms: they deliver more reliable, reproducible segmentations, thereby improving quantitative assessments of tumour-cell motility and enhancing downstream therapeutic or diagnostic research.