Point-Guided 3D U-SAM: MRI abdominal Segmentation Model using 3D Interactive U-Net and Segment Anything Model

Motivation: In abdominal MRI segmentation tasks, the need for high-quality support information for Segment Anything Model (SAM)-driven segmentation in limited data scenarios has motivated the search for an architecture with high performance and minimal support information requirements. Goal(s): Our objective is to design a user-friendly architecture for segmentation, focusing on using only support information within the region of interest. We aim to verify its high-performance capabilities. Approach: We developed Point-Guided 3D U-SAM, combining SAM and 3D U-Net with point-based support input. We compared its segmentation performance with existing methods. Results: The model excelled in abdominal MRI segmentation across various contrast levels, ensuring high performance. Impact: Point-Guided 3D U-SAM, which combines Segment Anything Model (SAM) and 3D U-Net with point-based inputs, would advance semi-automated organ segmentation, particularly where contrast is poor, such as MRCP, and in abdominal imaging, significantly reducing manual effort in clinical segmentation.