An Empirical Evaluation of Neural Network Architectures for 3D Spheroid Segmentation

The accurate segmentation of 3D spheroids is crucial in advancing biomedical research, particularly in understanding tumor development and testing therapeutic responses. As 3D spheroids emulate in vivo conditions more closely than traditional 2D cultures, efficient segmentation methods are essential for precise analysis. This study evaluates three prominent neural network architectures—U-Net, HRNet, and DeepLabV3+—for the segmentation of 3D spheroids, a critical challenge in biomedical image analysis. Through empirical analysis across a comprehensive Tumour Spheroid dataset, HRNet and DeepLabV3+ emerged as top performers, achieving high segmentation accuracy, with HRNet achieving 99.72% validation accuracy, a Dice coefficient of 96.70%, and a Jaccard coefficient of 93.62%. U-Net, although widely used in medical imaging, struggled to match the performance of the other models. The study also examines the impact of optimizers, with the Adam optimizer frequently causing overfitting, especially in U-Net models. Despite improvements with SGD and Adagrad, these optimizers did not surpass HRNet and DeepLabV3+. The study highlights the importance of selecting the right model–optimizer combination for optimal segmentation.