SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM (2304.05622v4)

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.

• The paper presents a novel SAMM architecture that bridges the SAM model with 3D Slicer for interactive, near real-time segmentation of medical images.
• It employs a robust communication framework using ZeroMQ and Numpy memory mapping to facilitate five parallel tasks with an end-to-end latency of approximately 0.6 seconds.
• The integration significantly enhances clinical imaging workflows by reducing manual annotation burdens and opens pathways for domain-specific model adaptations and text-prompt interactions.

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

The paper "SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM" presents an articulate development in the field of medical image segmentation by integrating the Segment Anything Model (SAM) with 3D Slicer, an image processing and visualization software. This collaboration seeks to enhance the applicability of foundation models in the nuanced domain of medical imaging by employing SAMM as a bridge to leverage SAM's capabilities within a well-established medical imaging platform.

Core Contributions and Methodology

The authors propose an architecture that hinges on two primary components: the SAM Server and an interactive prompt plugin for 3D Slicer (Slicer-IPP). With this integration, the pretrained SAM model receives volumetric medical image data from 3D Slicer, processes these inputs to compute embeddings, and subsequently utilizes prompts for near real-time segmentation. An innovative aspect of this integration is the adaptation of SAM to handle task-different medical images despite the original model's training on non-medical datasets, showcasing SAM's generalization potential across diverse domains.

From the technical perspective, the system employs ZeroMQ and Numpy memory mapping to ensure efficient communication between 3D Slicer and the SAM Server. This infrastructure facilitates five parallel tasks that contribute to the real-time segmentation process. Notably, the segmentation masks are synchronized with user interactions, such that they respond to prompt-based modifications almost instantaneously, a crucial feature for real-time applications.

Numerical Results and Performance

Empirical results underscore the potential of SAMM in efficiently handling medical image segmentation. The system achieves an end-to-end latency of approximately 0.6 seconds when processing medical images, specifically demonstrated using a magnetic resonance imaging (MRI) dataset. Furthermore, segmentation mask generation is facilitated in real-time after initial computations, underpinning the practical utility and readiness of this model for integration into clinical workflows.

Implications and Future Prospects

The introduction of SAMM into the landscape of medical image segmentation presents notable practical and theoretical implications. Practically, the integration promises to optimize workflows in medical imaging by reducing the manual burden of annotating large datasets, thanks to its semi-automatic capabilities driven by user prompts. Theoretically, the work ignites discussion on the broader application of foundation models like SAM in specialized domains, emphasizing the importance of adaptive infrastructures such as SAMM for domain-specific enhancements.

Looking forward, the paper hints at several avenues for future research, particularly the potential to refine SAM's performance on medical images by incorporating domain-specific datasets. Additionally, the authors suggest exploring text prompts as a mechanism to streamline user interaction with SAMM, as well as assessing the combination of SAM with other specialized models like MONAI to maximize their collective efficacy.

Conclusion

Overall, this research contributes to the advancement of medical imaging techniques by integrating an advanced image segmentation model with a versatile and widely-used platform. The work highlights the adaptability of foundation models for specific domains and presents a compelling case for further exploration and refinement of such integrations.