Gland Instance Segmentation

Gland instance segmentation in histology images aims to automatically identify and delineate individual glands within tissue samples, a crucial step for improving cancer diagnosis and treatment planning. Current research focuses on overcoming challenges like gland adhesion and irregular shapes using advanced techniques such as graph neural networks, dual encoders with boundary enhancement, and methods to leverage weakly-supervised learning to reduce the need for extensive manual annotation. These advancements hold significant promise for accelerating and improving the accuracy of pathological analysis, ultimately leading to better patient care.