A Systematic Review on Cell Nucleus Instance Segmentation

Abstract

Cell nucleus instance segmentation plays a pivotal role in medical research and clinical diagnosis by providing insights into cell morphology, disease diagnosis, and treatment evaluation. Despite significant efforts from researchers in this field, there remains a lack of a comprehensive and systematic review that consolidates the latest advancements and challenges in this area. In this survey, we offer a thorough overview of existing approaches to nucleus instance segmentation, exploring both traditional and deep learning-based methods. Traditional methods include watershed, thresholding, active contour model, and clustering algorithms, while deep learning methods include one-stage methods and two-stage methods. For these methods, we examine their principles, procedural steps, strengths, and limitations, offering guidance on selecting appropriate techniques for different types of data. Furthermore, we comprehensively investigate the formidable challenges encountered in the field, including ethical implications, robustness under varying imaging conditions, computational constraints, and the scarcity of annotated data. Finally, we outline promising future directions for research, such as privacy-preserving and fair AI systems, domain generalization and adaptation, efficient and lightweight model design, learning from limited annotations, as well as advancing multimodal segmentation models.