Anatomical Segmentation

Anatomical segmentation, the process of partitioning medical images into distinct anatomical structures, aims to automate the time-consuming and expert-dependent task of manual annotation. Current research focuses on improving segmentation accuracy and efficiency using deep learning models, including U-Net variants, transformers (like Swin UNETR and Detection Transformers), and graph convolutional networks, often incorporating techniques like self-supervised learning and weakly supervised methods to reduce reliance on large annotated datasets. These advancements are crucial for accelerating medical image analysis, enabling more precise diagnoses, personalized treatment planning, and improved understanding of anatomical variations across populations.