ArticleMedical image analysis2024
MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation.
Article in Medical image analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Benchmarking Generalizability in Deep Learning-Based White Matter Tract Segmentation.bioRxiv : the preprint server for biology · 2026Article
- Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future.Neurocomputing · 2026Article
- Multi-modal segment anything model (mmSAM) for tumor segmentation in multi-tracer oncologic PET/CT.EJNMMI physics · 2026Article
- Dual-stage 3D medical image segmentation integrating learnable prompt generation and memory attention.Scientific reports · 2026Article
- A generalist foundation model and database for open-world medical image segmentation.Nature biomedical engineering · 2026Article
- Efficient Medical Image Segmentation in Multisensor Imaging: A Survey in the Era of Mamba and Foundation Models.Sensors (Basel, Switzerland) · 2026Review
- Unleashing Diffusion and State Space Models for Medical Image Segmentation.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence real-time overall recognition model for total mesorectal excision.International journal of surgery (London, England) · 2026Article
- A parallel UNet integrating KAN and mamba for medical image segmentation.Scientific reports · 2026Article
- Zero-Shot Vertebral Instance Segmentation on DICOM Spine Radiographs Using Promptable Segment Anything Models.Journal of clinical medicine · 2026Article
- From diverse CT scans to generalization: towards robust abdominal organ segmentation.BMC medical imaging · 2026Article
- 3D TractFormer: 3D Direct Volumetric White Matter Tract Segmentation with Hybrid Channel-Wise Transformer.Sensors (Basel, Switzerland) · 2026Article
- StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT.NPJ digital medicine · 2026Article
- Structure-aware multi-task learning with domain generalization for robust vertebrae analysis in spinal CT.NPJ digital medicine · 2026Article
- Enhanced medical image segmentation using optimized bidirectional LSTM and dolphin partner optimizer.PloS one · 2026Article
- Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2.Machine learning: science and technology · 2025Article
- [Multimodal data processing through AI: envisioning the operating room of the future].Chirurgie (Heidelberg, Germany) · 2025Review
- TriSAM: Tri-Plane SAM for Zero-Shot Cortical Blood Vessel Segmentation in VEM Images.IEEE journal of biomedical and health informatics · 2025Article
- EchoFM: Foundation Model for Generalizable Echocardiogram Analysis.IEEE transactions on medical imaging · 2025Article
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13 authors.
Funding
Abstract
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM's performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM's pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM's pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. We comprehensively evaluate our method on five medical image segmentation tasks, by using 11 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://github.com/cchen-cc/MA-SAM.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.