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

TL;DR

SAMM integrates the Segment Anything Model into 3D Slicer to facilitate medical image segmentation, demonstrating near real-time inference and aiding development and validation in medical imaging applications.

Contribution

The paper introduces SAMM, a novel extension of SAM for 3D Slicer, enabling efficient medical image segmentation and validation.

Findings

Achieves 0.6-second latency for complete segmentation cycle

Enables near real-time inference on medical images

Open-source implementation available on GitHub

Abstract

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.