mlsanityData to 14 Oct 2026

SAM3D: Segment Anything Model in Volumetric Medical Images

arXiv 2309.03493 ↗First posted 7 Sep 2023eess.IV cs.CVcited by 34

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Abstract (arXiv)

Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominence, showcasing exceptional proficiency in processing medical imagery. Motivated by the Segment Anything Model (SAM)-a foundational model renowned for its remarkable precision and robust generalization capabilities in segmenting 2D natural images-we introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike current SAM-based methods that segment volumetric data by converting the volume into separate 2D slices for individual analysis, our SAM3D model processes the entire 3D volume image in a unified approach. Extensive experiments are conducted on multiple medical image datasets to demonstrate that our network attains competitive results compared with other state-of-the-art methods in 3D medical segmentation tasks while being significantly efficient in terms of parameters. Code and checkpoints are available at https://github.com/UARK-AICV/SAM3D.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 7 Sep 2023.

Builds on

25 of 36 references.

References · 25

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13 more
  1. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
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  6. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 280
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 16; 2025: 10; 2026: 3.

34 papers.

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  1. 2023SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Imagescited by 107
  2. 2024Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directionscited by 97
  3. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 64
  4. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 55
  5. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  6. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  7. 2024Segment anything model 2: an application to 2D and 3D medical imagescited by 28
  8. 2023I-MedSAM: Implicit Medical Image Segmentation with Segment Anythingcited by 22
  9. 2024Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Modelscited by 20
  10. 2024FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Imagescited by 14
  11. 2025Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptationcited by 12
  12. 2023I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnosescited by 10
22 more
  1. 2024M2Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimationcited by 10
  2. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  3. 2025Recent Advances in Medical Imaging Segmentation: A Surveycited by 8
  4. 2023Slide-SAM: Medical SAM Meets Sliding Windowcited by 6
  5. 2025MedicoSAM: Robust Improvement of SAM for Medical Imagingcited by 6
  6. 2024Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentationcited by 4
  7. 2024MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentationcited by 4
  8. 2024SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Imagescited by 3
  9. 2025OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretationcited by 3
  10. 2024MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentationcited by 2
  11. 2024Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotationscited by 2
  12. 2024SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentationcited by 2
  13. 2025Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approachescited by 2
  14. 2024Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decompositioncited by 1
  15. 2025Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3Dcited by 1
  16. 2026Opportunistic Promptable Segmentation: Leveraging Routine Radiological Annotations to Guide 3D CT Lesion Segmentationcited by 1
  17. 2024Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformercited by 0
  18. 2025UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTMcited by 0
  19. 2025TAGS: 3D Tumor-Adaptive Guidance for SAMcited by 0
  20. 2025A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPDcited by 0
  21. 20263D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrumcited by 0
  22. 2026MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentationcited by 0