mlsanityData to 14 Oct 2026

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

arXiv 2304.05622 ↗First posted 12 Apr 2023eess.IV cs.CV cs.LGcited by 31

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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.

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

4 of 11 references.

References · 4

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2023Segment Anythingcited by 8,234
  3. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  4. 2019Multi-scale self-guided attention for medical image segmentationcited by 84

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Cited by year

Citing papers per year: 2023: 18; 2024: 12; 2025: 1.

31 papers.

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  1. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  2. 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
  3. 2023Foundational Models Defining a New Era in Vision: A Survey and Outlookcited by 110
  4. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  5. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 82
  6. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  7. 2024Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decodingcited by 48
  8. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  9. 2023Segment Anything Model for Medical Images?cited by 39
  10. 2023Matte Anything: Interactive Natural Image Matting with Segment Anything Modelscited by 39
  11. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  12. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
19 more
  1. 2023Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Surveycited by 21
  2. 2023FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detectioncited by 19
  3. 2023Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaboratecited by 17
  4. 2024Interactive 3D Medical Image Segmentation with SAM 2cited by 17
  5. 2023A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decisioncited by 11
  6. 2023Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentationcited by 9
  7. 2025MedicoSAM: Robust Improvement of SAM for Medical Imagingcited by 6
  8. 2023TomoSAM: a 3D Slicer extension using SAM for tomography segmentationcited by 5
  9. 2023Segment Anything Model for Brain Tumor Segmentationcited by 4
  10. 2024Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Modelscited by 4
  11. 2024WPS-SAM: Towards Weakly-Supervised Part Segmentation with Foundation Modelscited by 4
  12. 2024SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Imagescited by 3
  13. 2023Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacycited by 2
  14. 2024A Roadmap Towards Automated and Regulated Robotic Systemscited by 2
  15. 2023Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populationscited by 1
  16. 2024Segment Any Medical Model Extendedcited by 1
  17. 2024SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Modelcited by 1
  18. 2024SlicerChat: Building a Local Chatbot for 3D Slicercited by 1
  19. 2024Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular Camera in situ for Navigation and Safe Insertion into Natural Openingscited by 0