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

arXiv: https://arxiv.org/abs/2304.05622 · HTML: https://mlsanity.com/paper/2304.05622/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Multi-scale self-guided attention for medical image segmentation](https://mlsanity.com/paper/1906.02849/) (2019)

## Built on by (most cited first, 30 of 31)

- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [On the Challenges and Perspectives of Foundation Models for Medical Image Analysis](https://mlsanity.com/paper/2306.05705/) (2023)
- [Foundational Models Defining a New Era in Vision: A Survey and Outlook](https://mlsanity.com/paper/2307.13721/) (2023)
- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://mlsanity.com/paper/2306.06211/) (2023)
- [Segment Anything Model for Medical Images?](https://mlsanity.com/paper/2304.14660/) (2023)
- [Matte Anything: Interactive Natural Image Matting with Segment Anything Models](https://mlsanity.com/paper/2306.04121/) (2023)
- [Artificial General Intelligence for Medical Imaging Analysis](https://mlsanity.com/paper/2306.05480/) (2023)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.03678/) (2023)
- [FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection](https://mlsanity.com/paper/2312.14465/) (2023)
- [Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate](https://mlsanity.com/paper/2304.09728/) (2023)
- [Interactive 3D Medical Image Segmentation with SAM 2](https://mlsanity.com/paper/2408.02635/) (2024)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation](https://mlsanity.com/paper/2306.08958/) (2023)
- [MedicoSAM: Robust Improvement of SAM for Medical Imaging](https://mlsanity.com/paper/2501.11734/) (2025)
- [TomoSAM: a 3D Slicer extension using SAM for tomography segmentation](https://mlsanity.com/paper/2306.08609/) (2023)
- [Segment Anything Model for Brain Tumor Segmentation](https://mlsanity.com/paper/2309.08434/) (2023)
- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [WPS-SAM: Towards Weakly-Supervised Part Segmentation with Foundation Models](https://mlsanity.com/paper/2407.10131/) (2024)
- [SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images](https://mlsanity.com/paper/2408.15224/) (2024)
- [Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy](https://mlsanity.com/paper/2308.14461/) (2023)
- [A Roadmap Towards Automated and Regulated Robotic Systems](https://mlsanity.com/paper/2403.14049/) (2024)
- [Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations](https://mlsanity.com/paper/2312.11775/) (2023)
- [Segment Any Medical Model Extended](https://mlsanity.com/paper/2403.18114/) (2024)
- [SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model](https://mlsanity.com/paper/2405.06786/) (2024)
- [SlicerChat: Building a Local Chatbot for 3D Slicer](https://mlsanity.com/paper/2407.11987/) (2024)

## Abstract (arXiv)

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.