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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, 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]
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“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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“…foundation models have seen significant interest in computer vision, benefit from pre-training techniques comprising self-supervised learning [25, 26], contrastive learning [27, 28], language-vision pre-training [29, 30], etc. Recently, SAM [4] pre-trained on over 11M images stands out as a…”
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Paranjape, J. N., Sikder, S., Vedula, S. S., & Patel, V. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78198-8_13 · Cited by 2
Li, B., Feng, S., Zhang, J., Chen, G., Huang, S., Li, S., & Zhang, Y. (2025) · Mathematics · doi:10.3390/math13111723 · Cited by 2
Self-citationBoosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image Classification
Huang, Y., Cheng, P., Tam, R. C., & Tang, X. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3569797 · Cited by 3
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Self-citation3D electroacoustic tomography image enhancement using deep learning with the SAM-Med3D encoder
Lang, Y., Buller, J., Xu, Y., Sun, L., Jiang, Z., Xiang, L., & Ren, L. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae077d · Cited by 2
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TAGS: 3D Tumor-Adaptive Guidance for SAM
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Segment Anything Model for medical image segmentation: A review
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Leveraging Self-Supervised Vision Transformers for Segmentation-Based Transfer Function Design
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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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Metadata
- 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
- 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
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