Advanced Deep learning Techniques for Biomedical Cell Segmentation

Accurate cell segmentation is essential for numerous biomedical imaging applications, including disease diagnosis, cell behaviour analysis, and tissue classification. This paper presents a comprehensive comparative analysis of various deep learning techniques for cell segmentation, highlighting their architectural innovations, strengths, and limitations. We examine U-Net and its prominent variants like U-Net++, Attention U-Net, Residual U-Net, 3D U-Net, and TransUNet and other advanced techniques, each of which introduces specific enhancements such as nested skip connections, attention modules, and transformer-based encoders. These modifications improve performance in complex imaging scenarios, particularly with overlapping cells and weak boundaries. Mask R-CNN is evaluated for its instance-aware segmentation capabilities, which enable it to distinguish individual cells in dense clusters. Its adaptability to various imaging modalities and effectiveness in real-world biomedical applications, such as cancer and histopathological analysis, are also discussed. The study further explores Vision Transformer (ViT) architectures like ViT-UNet, Swin Transformer, and hybrid CNN-transformer models. These approaches utilizes long-range dependency modelling to improve contextual understanding in cellular images, offering a promising balance between segmentation accuracy and computational cost. Additionally, Pentatonic methods, hybrid models integrating multi-scale feature extraction, ensemble learning, graph-based representations, attention mechanisms, and self-supervised learning are analyzed for their robustness and versatility. Through this comparative evaluation, the paper provides insights into the advantages and trade-offs of each approach, serving as a guide for selecting appropriate deep learning models tailored to specific biomedical segmentation tasks.