Gland Segmentation

Gland segmentation in pathology images aims to automatically identify and delineate individual glands, crucial for accurate cancer diagnosis and prognosis. Current research focuses on developing robust deep learning models, employing techniques like dual encoders, boundary-enhanced attention, and morphology-inspired semantic grouping to overcome challenges posed by variations in gland shape, overlapping structures, and limited annotated data. These advancements, including methods leveraging weakly-supervised learning, are improving segmentation accuracy and reducing the reliance on expensive manual annotation, thereby accelerating the development of AI-assisted diagnostic tools.