Cell Nucleus
Cell nuclei analysis is crucial for understanding cellular processes and diagnosing diseases, driving research into automated and accurate methods for nuclei segmentation and classification within diverse biological images. Current research focuses on developing robust and generalizable models, including foundation models and hybrid architectures combining traditional image processing with deep learning (e.g., convolutional neural networks), to overcome challenges posed by data heterogeneity and limited training data. These advancements are significantly impacting fields like digital pathology, enabling faster and more precise diagnoses, and facilitating single-cell analysis for improved understanding of cellular heterogeneity and responses to perturbations.