Pathological Segmentation

Pathological segmentation aims to automatically identify and delineate different tissue types and structures within microscopic images of diseased tissue, facilitating faster and more accurate diagnosis and research. Current research focuses on improving the flexibility and efficiency of segmentation models, employing deep learning architectures like dynamic networks and leveraging large vision-language models to adapt to diverse image characteristics and user prompts, including incorporating molecular information to enhance annotation accuracy. These advancements are crucial for accelerating the analysis of whole slide images, reducing reliance on expert manual annotation, and ultimately improving the speed and accuracy of disease diagnosis and treatment development.