Nucleus Segmentation

Nucleus segmentation, the automated identification and delineation of cell nuclei in microscopy images, is crucial for quantitative analysis in various biological and medical fields. Current research emphasizes developing robust and efficient algorithms, often employing convolutional neural networks (CNNs), transformers, and hybrid architectures, to address challenges like overlapping nuclei, diverse staining protocols, and limited annotated data. These advancements improve accuracy and speed, facilitating large-scale image analysis in applications such as cancer diagnosis, drug discovery, and developmental biology, ultimately accelerating scientific discovery and improving healthcare.