Cell Segmentation

Cell segmentation, the process of identifying and outlining individual cells within images, is crucial for various biological and medical applications, including disease diagnosis, drug discovery, and quantitative analysis of cellular behavior. Current research focuses on improving segmentation accuracy and efficiency using deep learning architectures like U-Net and Transformers, often incorporating techniques such as test-time training, uncertainty awareness, and large convolution kernels to handle complex cell morphologies and diverse imaging modalities. These advancements are significantly impacting fields like pathology and cell biology by enabling high-throughput analysis of large datasets and facilitating more precise and objective measurements of cellular features.