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Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

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arxiv 2304.12620 v7 pith:PFR62IQD submitted 2023-04-25 cs.CV

Junde Wu , Wei Ji , Yuanpei Liu , Huazhu Fu , Min Xu , Yanwu Xu , Yueming Jin This is my paper
classification cs.CV
keywords medicalsegmentationimagemodeladaptermed-saadaptationanything
verification ladder T0 review T1 audit T2 compute T3 formal
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The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2\% of the parameters. Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.

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Cited by 16 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition

    cs.CV 2025-09 conditional novelty 6.0 of 10

    SegSLR uses pose-guided SAM 2 video segmentations to focus RGB streams on the signer's body and hands, improving isolated sign language recognition on ChaLearn249 IsoGD.

  2. MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    MetaScope, an optics-driven network, corrects metalens endoscope images and outperforms prior methods on segmentation and restoration.

  3. Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SAM-MaGuP, a SAM-based polyp segmentation model with a 1D-2D Mamba adapter and boundary distillation, reports state-of-the-art mDice/mIoU on five public colonoscopy datasets.

  4. PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism

    eess.IV 2025-07 reject novelty 6.0 of 10

    Prompt2SegCXR is a lightweight dual-input model that segments six organs and seventeen diseases in chest X-rays from hand-drawn doodle prompts, alongside a new 23-class prompt dataset.

  5. LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LoD-Loc v2 localizes aerial cameras by aligning predicted building silhouettes with rendered low-detail city-model silhouettes, achieving accurate 4-DoF pose without textured maps.

  6. SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    SRPL-SFDA refines source-model pseudo-labels with SAM and consistency-based reliability selection, reporting near-supervised adaptation on two MRI segmentation benchmarks.

  7. SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost

    cs.CV 2025-06 conditional novelty 6.0 of 10

    SAM-I2V upgrades SAM to video segmentation with three lightweight modules (temporal integrator, selective memory, memory prompts), reaching about 90% of SAM 2.1's average J&F at 0.2% of its training cost.

  8. Multimodal SAM-adapter for Semantic Segmentation

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A side-tuning adapter injects RGB-plus-auxiliary-sensor fused features into SAM's encoder, reaching state-of-the-art semantic segmentation on DeLiVER, FMB, and MUSES.

  9. ParticleSAM: Small Particle Segmentation for Material Quality Monitoring in Recycling Processes

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    ParticleSAM adapts the Segment Anything Model to segment small, dense particles and introduces a simulated multi-particle dataset for recycling quality control.

  10. Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model

    cs.CV 2025-08 reject novelty 5.0 of 10

    PromptReg registers two images by estimating point prompts that make SAM segment the same anatomical regions in both, without any training.

  11. Region-aware Depth Scale Adaptation with Sparse Measurements

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A non-learning method segments an image and gives each region its own scale and shift, fitted to a few sparse depth points, to turn relative monocular depth predictions into metric depth more accurately than a single ...

  12. Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    MA-SAM2 adds context-aware and occlusion-resilient memory to SAM2 and reports Challenge IoU of 62.49 percent on EndoVis2017 and 64.40 percent on EndoVis2018, beating SAM2 by 6.10 and 4.36 points.

  13. TAGS: 3D Tumor-Adaptive Guidance for SAM

    eess.IV 2025-05 conditional novelty 5.0 of 10

    A SAM-based 3D tumor segmentation framework combining TotalSegmentator organ masks, CLIP text guidance, and multi-stage adapters outperforms several medical segmentation baselines on three CT datasets.

  14. Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation

    cs.CV 2025-06 conditional novelty 4.0 of 10

    LoRA-fine-tuned SAM outperforms Mask R-CNN for agricultural field delineation on AI4B and on the new ERAS dataset, with limited degradation on 2024 data.

  15. SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

    cs.CV 2025-06 reject novelty 4.0 of 10

    SSS applies SAM-2 with a Discriminative Feature Enhancement mechanism and a physical-constraint sliding-window prompt generator, reporting Dice scores of 53.15 on BHSD and 89.34 to 91.21 on ACDC.

  16. Evolutionary computing-based image segmentation method to detect defects and features in Additive Friction Stir Deposition Process

    cs.CV 2025-06 reject novelty 3.0 of 10

    A PSO-based image segmentation workflow was applied to AFSD micrographs, producing per-sample thresholds and gradient-attention visualizations that the authors interpret as defect indicators, without independent validation.

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