3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
Open accessAbstract
Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and unstable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original SAM architecture is designed for 2D natural images and, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively. In this paper, we propose a novel adaptation method for transferring SAM from 2D to 3D for promptable medical image segmentation. Through a holistically designed scheme for architecture modification, we transfer the SAM to support volumetric inputs while retaining the majority of its pre-trained parameters for reuse. The fine-tuning process is conducted in a parameter-efficient manner, wherein most of the pre-trained parameters remain frozen, and only a few lightweight spatial adapters are introduced and tuned. Regardless of the domain gap between natural and medical data and the disparity in the spatial arrangement between 2D and 3D, the transformer trained on natural images can effectively capture the spatial patterns present in volumetric medical images with only lightweight adaptations. We conduct experiments on four open-source tumor segmentation datasets, and with a single click prompt, our model can outperform domain state-of-the-art medical image segmentation models and interactive segmentation models. We also compared our adaptation method with existing popular adapters and observed significant performance improvement on most datasets. Our code and models are available at: https://github.com/med-air/3DSAM-adapter.
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Most promptable medical segmentation methods are still developed and evaluated around clean-prompt protocols.
“Most promptable medical segmentation methods, however, are still developed and evaluated around clean-prompt protocols.”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [5]
Section 3.3 (page unavailable) says accurate prompt guidance is difficult in medical images and that a model can fail when prompts are inaccurate, supporting the concern about reliance on clean prompts. It does not establish that most promptable medical segmentation methods are developed and evaluated under clean-prompt protocols. (LLM, conf=0.75)
Experimental — accuracy not yet measured3DSAM-Adapter and SAM-Med3D adapt SAM-style prompting to 3D medical images, while ProMISe is a prompt-driven 3D medical segmentation method based on pretrained image foundation models.
“3DSAM-Adapter [5] and SAM-Med3D [29] adapt SAM-style prompting to 3D medical images, while ProMISe [17] is a prompt-driven 3D medical segmenta-tion method based on pretrained image foundation models.”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [5]
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Segment Anything Model (SAM) for Medical Image Segmentation: A Preliminary Review
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Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
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SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images
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UV-SAM: Adapting Segment Anything Model for Urban Village Identification
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EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting
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Computer Vision-Based cybernetics systems for promoting modern poultry Farming: A critical review
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LeSAM: Adapt Segment Anything Model for Medical Lesion Segmentation
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Distilling Semantic Priors from SAM to Efficient Image Restoration Models
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Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning
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ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object
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Data-Centric Foundation Models in Computational Healthcare: A Survey
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A Foundation Model for Lesion Segmentation on Brain MRI With Mixture of Modality Experts
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PQ-SAM: Post-training Quantization for Segment Anything Model
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One Registration is Worth Two Segmentations
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Semi-supervised TEE Segmentation via Interacting with SAM Equipped with Noise-Resilient Prompting
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TAGS: 3D Tumor-Adaptive Guidance for SAM
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“…et al., 2023; Ravi et al., 2024) represented a pivotal breakthrough, enabling high-quality segmentation through interactive human prompts (e.g., points, bounding boxes, or masks) (Ma et al., 2024; Wei et al., 2024b; Konwer et al., 2025; Ma et al., 2025b; Wu et al., 2025a; Gong et al., 2024).”
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ZAP-2.5DSAM: zero additional parameters advancing 2.5D SAM adaptation to 3D tumor segmentation
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LRD-3DSAM: A Novel SAM-Based 3D Medical Segmentation Network for Modeling Long-Range Dependencies
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- Authors
- Gong, S., Zhong, Y., Ma, W., Li, J., Wang, Z., Zhang, J., Heng, P.-A., & Dou, Q.
- Affiliations
- Chinese University of Hong Kong (HK)
- Corresponding author
- Dou, Qi
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 98
- Pages
- 103324
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W4401852105
- Cited by
- 156
- References
- 72
- Citations by year
- 2026: 51 · 2025: 61 · 2024: 41 · 2023: 3
- FWCI
- 21.42
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- Segment Anything Model, 3D medical image segmentation, tumor segmentation, prompt-based segmentation, parameter-efficient fine-tuning, volumetric medical data, interactive segmentation, domain adaptation
- MeSH terms
- Algorithms, Humans, Neoplasms, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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