Abdominal Multi Organ Segmentation

Abdominal multi-organ segmentation aims to automatically identify and delineate various organs within abdominal CT or MRI scans, facilitating improved disease diagnosis and treatment planning. Current research heavily utilizes deep learning, focusing on architectures like U-Net and transformers, with modifications such as dynamic kernel sizes, attention mechanisms, and boundary-aware strategies to improve accuracy and robustness, particularly for challenging cases with ambiguous organ boundaries or missing organs. This field is crucial for advancing medical image analysis, enabling more efficient and accurate diagnoses, and reducing the burden on medical professionals through automation.