Tissue Segmentation

Tissue segmentation, the process of partitioning digital images of biological tissue into constituent regions, aims to automate the analysis of microscopic images for faster and more objective diagnoses. Current research heavily utilizes deep learning, employing architectures like U-Nets, transformers, and generative adversarial networks (GANs) to achieve accurate segmentation across diverse imaging modalities (e.g., MRI, ultrasound, histology) and tissue types. This work is crucial for accelerating research in various fields, including oncology, neurology, and pathology, by enabling high-throughput analysis of large image datasets and assisting clinicians in diagnosis and treatment planning. Furthermore, efforts are underway to improve model generalizability and robustness to variations in imaging protocols and staining techniques through techniques like domain adaptation and self-supervised learning.