Abdominal Organ Segmentation

Abdominal organ segmentation, the automated identification and delineation of organs in abdominal medical images (CT and MRI scans), aims to improve diagnostic accuracy and streamline clinical workflows. Current research emphasizes improving model robustness and generalization across diverse patient populations and imaging protocols, often employing deep learning architectures like U-Nets and Transformers, along with techniques such as unsupervised domain adaptation and self-training to address data scarcity and variability. These advancements hold significant promise for accelerating diagnosis, treatment planning, and quantitative organ analysis, ultimately improving patient care.