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The Segment Anything Model (SAM) is a recently developed large model forgeneral-purpose segmentation for computer vision tasks. SAM was trained using11 million images with over 1 billion masks and can produce segmentationresults for a wide range of objects in natural scene images. SAM can be viewedas a general perception model for segmentation (partitioning images intosemantically meaningful regions). Thus, how to utilize such a large foundationmodel for medical image segmentation is an emerging research target. This papershows that although SAM does not immediately give high-quality segmentation formedical image data, its generated masks, features, and stability scores areuseful for building and training better medical image segmentation models. Inparticular, we demonstrate how to use SAM to augment image input forcommonly-used medical image segmentation models (e.g., U-Net). Experiments onthree segmentation tasks show the effectiveness of our proposed SAMAug method.The code is available at \url{https://github.com/yizhezhang2000/SAMAug}.

Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model