The DOI matches its Crossref record; 68 of the 72 references it cites were found in a registry or an index.
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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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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Cites
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 13,453
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477
Emerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638 · cited 1×
“…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…”
BackgroundLiu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 2×
“The success of SAM can be partly ascribed to the powerful prompt engineering, which originates from large language models [12] with recent variants improving model generalizability across tasks.”
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Cited by
Sorted by citations
Medical SAM adapter: Adapting segment anything model for medical image segmentation
Wu, J., Wang, Z., Hong, M., Ji, W., Fu, H., Xu, Y., Xu, M., & Jin, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103547 · Cited by 1,187
Self-citationSegment anything model for medical image segmentation: Current applications and future directions
Zhang, Y., Shen, Z., & Jiao, R. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108238 · Cited by 364
On the challenges and perspectives of foundation models for medical image analysis
Zhang, S., & Metaxas, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102996 · Cited by 340
MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation
Chen, C., Miao, J., Wu, D., Zhong, A., Yan, Z., Kim, S., Hu, J. J., Liu, Z., Sun, L., Li, X., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103310 · Cited by 293
Self-citationFoundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions
He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3496744 · Cited by 162
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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