Cell Tracking

Cell tracking in microscopy videos aims to automatically identify and follow individual cells over time, providing crucial information for understanding biological processes. Current research emphasizes improving tracking accuracy and robustness, particularly in dense environments or when cells divide, using deep learning architectures like transformers and recurrent neural networks, often integrated with mathematical models or graph-based representations. These advancements are significantly impacting biomedical research by enabling high-throughput analysis of cell behavior and lineage, facilitating discoveries in areas such as developmental biology and disease research. Improved metrics for evaluating tracking performance are also a key area of focus.