Glomerulus Segmentation

Glomerulus segmentation in digital pathology aims to automatically identify and delineate glomeruli—the kidney's filtering units—within high-resolution whole slide images (WSIs), facilitating faster and more objective diagnosis of kidney diseases. Current research emphasizes developing robust and efficient deep learning models, including adaptations of U-Net and YOLO architectures, and exploring semi-supervised and transfer learning techniques to address data scarcity and annotation challenges, particularly for pathological glomeruli. Accurate glomerulus segmentation promises to improve the speed and consistency of kidney disease diagnosis, aiding pathologists in analyzing complex tissue structures and enabling quantitative assessments of glomerular lesions for better disease management.