Nucleus Instance Segmentation

Nucleus instance segmentation aims to automatically identify and delineate individual cell nuclei within microscopic images, a crucial task in digital pathology for disease diagnosis and research. Current research focuses on improving the accuracy and efficiency of this process, exploring various deep learning architectures such as U-Net variations, Vision Transformers (ViTs), and foundation models like the Segment Anything Model (SAM), often incorporating techniques like attention mechanisms, contrastive learning, and model pruning for enhanced performance and reduced computational cost. Advances in this field promise to significantly accelerate and improve the analysis of histopathology images, aiding pathologists in diagnosis and enabling large-scale quantitative studies of tissue samples.