SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM
This provides a tool for the medical imaging community to assess and use SAM on medical data, but it is incremental as it primarily integrates an existing model into software.
The paper tackles the need to validate and apply the Segment Anything Model (SAM) on medical images by introducing SAMM, a 3D Slicer integration that achieves 0.6-second latency for real-time mask inference.
The Segment Anything Model (SAM) is a new image segmentation tool trained with the largest available segmentation dataset. The model has demonstrated that, with prompts, it can create high-quality masks for general images. However, the performance of the model on medical images requires further validation. To assist with the development, assessment, and application of SAM on medical images, we introduce Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer - an image processing and visualization software extensively used by the medical imaging community. This open-source extension to 3D Slicer and its demonstrations are posted on GitHub (https://github.com/bingogome/samm). SAMM achieves 0.6-second latency of a complete cycle and can infer image masks in nearly real-time.