Accurate and efficient cell detection remains challenging due to variations in cell types, experimental conditions, and research objectives. There is a need for comparative analyses to identify optimal segmentation methods tailored to specific applications. This study evaluates three widely used segmentation methods—U-NET, Detectron2 Mask-RCNN50, and YOLOv11—in the context of segmenting HeLa cells, which are extensively used to define cell morphology, support time-lapse imaging, and enable cell tracking. Using pixel-based accuracy metrics, Detectron2 Mask-RCNN50 demonstrated the highest accuracy on the test dataset. YOLOv11 showed moderate performance with a pixel accuracy of 82.97%, whereas U-NET exhibited a significant decline, achieving only 66.40% accuracy on the test data. When comparing object-based accuracy, Detectron2 reached 84.05% accuracy, whereas YOLOv11 reached only 37.31%.