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The Segment Anything Model (SAM) is a new image segmentation tool trainedwith the largest available segmentation dataset. The model has demonstratedthat, with efficient prompting, it can create high-quality, generalized masksfor image segmentation. However, the performance of the model on medical imagesrequires further validation. To assist with the development, assessment, andapplication of SAM on medical images, we introduce Segment Any Medical Model(SAMM), an extension of SAM on 3D Slicer - an open-source image processing andvisualization software extensively used by the medical imaging community. Thisopen-source extension to 3D Slicer and its demonstrations are posted on GitHub(https://github.com/bingogome/samm). SAMM achieves 0.6-second latency of acomplete cycle and can infer image masks in nearly real-time.

SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM