Snapshot 2026-08-04 16:17:00 UTC · version 1
Segment Anything Model (SAM) uses vision transformer-based image encoder to extract image features and compute an image embedding, and prompt encoder to embed prompts and incorporate user interactions. Then extranted information from two encoders are combined to alightweight mask. Use it to navigate the topic and choose relevant methods, papers or tools.
Segment Anything Model (SAM) uses vision transformer-based image encoder to extract image features and compute an image embedding, and prompt encoder to embed prompts and incorporate user interactions. Then extranted information from two encoders are combined to alightweight mask. Use it to navigate the topic and choose relevant methods, papers or tools.
Editorial note: curated source snapshot published by Collider.club under the MIT License. Source attribution is preserved in the front matter.
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The introduction of the Segment Anything Model (SAM) (paper) and subsequent SAM2 (paper) & SAM3 (paper) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image/video segmentation, introducing a plethora of previously unexplored capabilities.
This repo will continue to track and summarize the latest research progress of SAM & Foundation Models in medical image segmentation to support ongoing research endeavors. If you find this project helpful, please consider stars or citing. Feel free to contact for any suggestions. If you would like to contribute, please open an issue.
[24.1] We provide a comprehensive survey of recent endeavors aimed at extending the efficacy of SAM to medical image segmentation tasks, encompassing both empirical benchmarking and methodological adaptations. Additionally, we explore potential avenues for future research directions in SAM's role within medical image segmentation. Please refer to the paper (CIBM'24) for more details.
[26.4] Our latest survey on the development, adaptation, and application of generalist segmentation foundation models in biomedical image and video analysis is published online at (IRAD'26).
@article{SAM4MIS,
title={Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions},
author={Zhang, Yichi and Shen, Zhenrong and Jiao, Rushi},
journal={Computers in Biology and Medicine},
volume={171},
pages={108238},
year={2024}
}
@article{zhang2026gsfm,
title={Unleashing the Potential of Generalist Segmentation Foundation Models for Biomedical Image and Video Analysis},
author={Zhang, Yichi and Shen, Zhenrong and Li, Lanlan and Zhang, Wenbo and Xue, Le},
journal={iRadiology},
year={2026}
}
Segment Anything Model (SAM) uses vision transformer-based image encoder to extract image features and compute an image embedding, and prompt encoder to embed prompts and incorporate user interactions. Then extranted information from two encoders are combined to alightweight mask decoder to generate segmentation results based on the image embedding, prompt embedding, and output token. For more details, please refer to the original paper of SAM.
A brief chronology of Segment Anything Model (SAM) and its variants for medical image segmentation in 2023.
| Date | Authors | Title | Code |
|---|---|---|---|
| 202602 | R. Zhai et al. | SAM2-driven dual-teacher framework using hierarchical cross-slice context for semi-supervised 3D medical image segmentation (paper) | None |
| 202511 | W. Su et al. | Zero-Shot Capillary Segmentation in Dermoscopy Images via SAM2: A Case Study on Oral Mucosa (paper) | None |
| 202511 | H. Buyukpatpat et al. | A Comparative Evaluation of Zero-Shot Performance of SAM, SAM2, MedSAM, and MedSAM2 Models on Lung Segmentation (paper) | None |
| 202511 | S. Chakrabarty et al. | Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data (paper) | None |
| 202511 | A. Liu et al. | MedSAM3: Delving into Segment Anything with Medical Concepts (paper) | Code |
| 202511 | M. Shokri et al. | Zero-shot Stroke Lesion Segmentation via CAM-guided Prompting of MedSAM2 (paper) | None |
| 202511 | X. Yao et al. | Towards Better Ultrasound Video Segmentation Foundation Model: An Empirical study on SAM2 Finetuning from Data Perspective (paper) | None |
| 202510 | L. Guo et al. | ESAM2-BLS: Enhanced Segment Anything Model 2 for Efficient Breast Lesion Segmentation in Ultrasound Imaging (paper) | None |
| 202508 | M. Fernandez et al. | SAM 2-Driven Self-Training for Mammogram Segmentation: Zero-Shot Mask Generation Via Pseudo-Video (paper) | Code |
| 202508 | Y. Chen et al. | SAM2Med3D: Leveraging video foundation models for 3D breast MRI segmentation (paper) | None |
| 202508 | Z. Wu et al. | Vessel-SAM2: Adapting Segment Anything 2 for Patch-Free Retinal Vessel Segmentation in Ultra-High Resolution Fundus Images (paper) | None |
| 202508 | J. He et al. | Training-Free Breast Ultrasound Image Segmentation with Retrieval-based SAM2 (paper) | None |
| 202507 | C. Wang et al. | FreqSAM2-UNet: Adapter Fine-Tuning Frequency-Aware Network of SAM2 for Universal Medical Segmentation (paper) | None |
| 202507 | G. Xu et al. | Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2 (paper) | Code |
| 202507 | E. Chukwujindu et al. | Improving Medical Image Segmentation with SAM2: Analyzing the Impact of Object Characteristics and Finetuning on Multi-Planar Datasets (paper) | Code |
| 202507 | B. Podvin et al. | SAMUSA: Segment Anything Model 2 for UltraSound Annotation (paper) | None |
| 202506 | X. Yu et al. | CRISP-SAM2 : SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation (paper) | Code |
| 202505 | M. Mansoori et al. | Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models (paper) | Code |
| 202505 | G. Huo et al. | SAMba-UNet: Synergizing SAM2 and Mamba in UNet with Heterogeneous Aggregation for Cardiac MRI Segmentation (paper) | None |
| 202504 | Y. Chen et al. | Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2 (paper) | None |
| 202504 | Y. Yamagishi et al. | Using Segment Anything Model 2 for Zero-Shot 3D Segmentation of Abdominal Organs in Computed Tomography Scans to Adapt Video Tracking Capabilities for 3D Medical Imaging: Algorithm Development and Validation (paper) | None |
| 202504 | J. Ma et al. | MedSAM2: Segment Anything in 3D Medical Images and Videos (paper) | Code |
| 202504 | JD. Gutiérrez et al. | Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt (paper) | None |
| 202504 | A. Kazemi et al. | Semi-automated segmentation of magnitude images in 4D flow MR scans using segment anything model 2 (SAM 2) (paper) | None |
| 202503 | S. Wei et al. | Self-Prompting Driven SAM2 for 3D Medical Image Segmentation (paper) | None |
| 202503 | H. Zu et al. | Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching (paper) | None |
| 202502 | X. Wang et al. | Proxy Prompt: Endowing SAM & SAM2with Auto-Interactive-Prompt for Medical Segmentation (paper) | None |
| 202502 | B. Xie et al. | RFMedSAM2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 (paper) | None |
| 202501 | G. Hoyer et al. | Scalable Evaluation Framework for Foundation Models in Musculoskeletal MRI Bridging Computational Innovation with Clinical Utility (paper) | None |
| 202501 | X. He et al. | Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation (paper) | None |
| 202411 | Z. Li et al. | Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images (paper) | None |
| 202408 | M. Mansoori et al. | Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model (paper) | Code |
| 202408 | X. Chen et al. | SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2 (paper) | Code |
| 202408 | L. Zhao et al. | Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models (paper) | None |
| 202408 | Z. Yildiz et al. | SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images (paper) | Code |
| 202408 | Y. He et al. | A Short Review and Evaluation of SAM2’s Performance in 3D CT Image Segmentation (paper) | Code |
| 202408 | X. Xiong et al. | SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation (paper) | Code |
| 202408 | H. Liu et al. | Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning (paper) | Code |
| 202408 | Y. Yamagishi et al. | Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging (paper) | None |
| 202408 | M. Mansoori et al. | Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection (paper) | Code |
| 202408 | AS. Yu et al. | Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2 (paper) | None |
| 202408 | J. Yu et al. | SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation (paper) | None |
| 202408 | T. Chen et al. | SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More (paper) | None |
| 202408 | S. Sengupta et al. | Is SAM 2 Better than SAM in Medical Image Segmentation? (paper) | None |
| 202408 | Y. Shen et al. | Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation (paper) | None |
| 202408 | M. Zhang et al. | SAM2-PATH: A better segment anything model for semantic segmentation in digital pathology (paper) | Code |
| 202408 | J. Ma et al. | Segment Anything in Medical Images and Videos: Benchmark and Deployment (paper) | Code |
| 202408 | Z. Yan et al. | Biomedical SAM 2: Segment Anything in Biomedical Images and Videos (paper) | Code |
| 202408 | C. Shen et al. | Interactive 3D Medical Image Segmentation with SAM 2 (paper) | Code |
| 202408 | A. Lou et al. | Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2 (paper) | Code |
| 202408 | J. Zhu et al. | Medical SAM 2: Segment medical images as video via Segment Anything Model 2 (paper) | Code |
| 202408 | H. Dong et al. | Segment anything model 2: an application to 2D and 3D medical images (paper) | None |
| Date | Authors | Title | Code |
|---|---|---|---|
| 202604 | Y. Zhang et al. | SemiSAM-O1: How far can we push the boundary of annotation-efficient medical image segmentation? (paper) | Code |
| 202604 | S. Huang et al. | PromptReg: Interactive Registration by “Corresponding Prompts” for Segment Anything Model (SAM) (paper) | None |
| 202604 | J. Peng et al. | MedP-CLIP: Medical CLIP with Region-Aware Prompt Integration (paper) | None |
| 202603 | T. Tang et al. | HATSAM: hierarchical adaptation strategy for segment anything model in medical imaging (paper) | None |
| 202603 | J. Hasan et al. | FM-Adapt: Foundation model adaptation with photoacoustic-supervised learning for interventional ultrasound (paper) | Code |
| 202603 | K. Borst et al. | Are General-Purpose Vision Models All We Need for 2D Medical Image Segmentation? A Cross-Dataset Empirical Study (paper) | Code |
| 202603 | K. Phuntsho et al. | Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs (paper) | None |
| 202603 | J. Tang et al. | Distillation-SAM: Knowledge Distillation Based Auto-prompt Embedding Learning for Surgical Image Segmentation (paper) | None |
| 202603 | C. Magg et al. | Prompting with the human-touch: evaluating model-sensitivity of foundation models for musculoskeletal CT segmentation (paper) | None |
| 202602 | C. Krishnan et al. | SynSAM: a hybrid synchronous learning framework with knowledge retention for prostate zonal segmentation leveraging the segment anything model (paper) | None |
| 202602 | R. Ge et al. | Generative data-engine foundation model for universal few-shot 2D vascular image segmentation (paper) | Code |
| 202602 | M. Liu et al. | StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT (paper) | None |
| 202602 | J. Miao et al. | SAM-driven cross prompting with adaptive sampling consistency for semi-supervised medical image segmentation (paper) | Code |
| 202601 | J. Li et al. | Semi-MedSAM: Adapting SAM-assisted semi-supervised multi-modality learning for medical endoscopic image segmentation (paper) | None |
| 202601 | S. Chattopadhyay et al. | On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Prompts (paper) | Code |
| 202601 | S. Tang et al. | TA-MedSAM: Text-augmented improved MedSAM for pulmonary lesion segmentation (paper) | None |
| 202601 | J. Zhu et al. | AutoPromptSeg: Automated Decoupling of Uncertainty Prompts with SAM for semi-supervised medical image segmentation (paper) | None |
| 202601 | S. Ahn et al. | Adaptability of Vision Foundation Models for 3D Medical Image Segmentation (paper) | None |
| 202512 | Z. Zhang et al. | Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation (paper) | None |
| 202512 | M. Marks et al. | CellSAM: a foundation model for cell segmentation (paper) | Code |
| 202511 | M. Rokuss et al. | VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation (paper) | Code |
| 202511 | Q. Tong et al. | MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images (paper) | Code |
| 202511 | T. Shaharabany et al. | WS-SAM: Segment Anything with Sparse Prompts for Weakly-Supervised Medical Segmentation (paper) | None |
| 202511 | FP. Salanitri et al. | SAM-guided prompt learning for Multiple Sclerosis lesion segmentation (paper) | None |
| 202511 | T. Jiang et al. | UltraSAM: A foundational medical ultrasound segmentation model with limited training data (paper) | None |
| 202511 | C. Xu et al. | Boundary-Aware Test-Time Adaptation for Zero-Shot Medical Image Segmentation (paper) | Code |
| 202511 | K. Moore et al. | Not Quite Anything: Overcoming SAM’s Limitations for 3D Medical Imaging (paper) | None |
| 202511 | Y. Zhang et al. | Continual Alignment for SAM: Rethinking Foundation Models for Medical Image Segmentation in Continual Learning (paper) | Code |
| 202510 | Y. Zhang et al. | SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images (paper) | Code |
| 202510 | J. Hu et al. | Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D (paper) | Code |
| 202510 | S. Chen et al. | SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images (paper) | Code |
| 202510 | Y. Zhang et al. | Enhancing the Reliability of Auto-Prompting SAM for Medical Image Segmentation with Uncertainty Estimation and Rectification (paper) | None |
| 202510 | AK. Shibu et al. | MedSAM-Guided Curriculum Learning for White Matter Tract Segmentation in Block Face Imaging of Fetal Brain (paper) | None |
| 202510 | M. Attari et al. | SAM-SPJunc: Self-Prompting for Junction Detection in Retinal Images via Radius-Based Representations (paper) | None |
| 202510 | H. Chi et al. | Cross-Hierarchical Decoding with SAM for Semi-Supervised Medical Image Segmentation (paper) | None |
| 202510 | Y. Yang et al. | FM2: Fusing multiple foundation models for pathology image analysis via disentangled consensus-divergence representation (paper) | None |
| 202510 | Q. Xu et al. | De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation (paper) | Code |
| 202510 | Y. Yang et al. | SAM2-3dMed: Empowering SAM2 for 3D Medical Image Segmentation (paper) | None |
| 202510 | TC. Ndir et al. | Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training (paper) | None |
| 202509 | G. Zhang et al. | IPLC+: SAM-Guided Iterative Pseudo Label Correction for Source-Free Domain Adaptation in Medical Image Segmentation (paper) | Code |
| 202509 | Z. Liu et al. | BALR-SAM: Boundary-Aware Low-Rank Adaptation of SAM for Resource-Efficient Medical Image Segmentation (paper) | None |
| 202509 | J. Zhuang et al. | Bio2Vol: Adapting 2D Biomedical Foundation Models for Volumetric Medical Image Segmentation (paper) | Code |
| 202509 | Z. Peng et al. | HA-SAM: Hierarchically Adapting SAM for Nerve Segmentation in Ultrasound Images (paper) | None |
| 202509 | Y. Wang et al. | Collect vascular specimens in one cabinet: A hierarchical prompt-guided universal model for 3D vascular segmentation (paper) | Code |
| 202509 | Y. Zhang et al. | Embedded framework for clinical medical image segment anything in resource limited healthcare regions (paper) | Code |
| 202509 | L. Li et al. | Segment Anything Model for Gastric Cancer (paper) | None |
| 202509 | Z. Tu et al. | Spatial-Temporal Memory Filtering SAM for Lesion Segmentation in Breast Ultrasound Videos (paper) | Code |
| 202509 | R. Li et al. | MoE-SAM: Enhancing SAM for Medical Image Segmentation with Mixture-of-Experts (paper) | Code |
| 202509 | Q. Li et al. | From Generalist to Specialist: Distilling a Mixture of Foundation Models for Domain-Specific Medical Image Segmentation (paper) | None |
| 202509 | M. Zhang et al. | Towards Robust Retinal Vessel Segmentation via Reducing Open-Set Label Noises from SAM-Generated Masks (paper) | None |
| 202509 | H. Su et al. | Sparsely Annotated Medical Image Segmentation via Cross-SAM of 3D and 2D Networks (paper) | Code |
| 202509 | T. Wang et al. | pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation (paper) | None |
| 202509 | C. Qin et al. | DUR-Net+: Semi-Supervised Abdominal CT Pheochromocytoma Segmentation Via Dynamic Uncertainty Rectified and Prior Knowledge From SAM-Med3D (paper) | None |
| 202509 | D. Chen et al. | SAMTNU: Adaptive Segment Anything Model for Thyroid and Nodule Ultrasound Image Segmentation (paper) | None |
| 202509 | X. Zhang et al. | Leveraging Multi-Text Joint Prompts in SAM for Robust Medical Image Segmentation (paper) | None |
| 202509 | T. Ward et al. | A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation (paper) | Code |
| 202509 | X. Yu et al. | Medical SAM-Clip Grafting for brain tumor segmentation (paper) | None |
| 202508 | S. Zhang et al. | A generalist foundation model and database for open-world medical image segmentation (paper) | Code |
| 202508 | Z. Zhao et al. | Large-vocabulary segmentation for medical images with text prompts (paper) | Code |
| 202508 | AA. Shami et al. | Persistent Homology and Segment Anything Model for Automated Zero-Shot Localized Medical X-ray Images Segmentation (PH-SAM) (paper) | None |
| 202508 | B. Huang et al. | E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation (paper) | Code |
| 202508 | X. Sun et al. | MFB-SAC: A Multi-Scale Frequency and Boundary-Enhanced SAM for Cell Segmentation (paper) | Code |
| 202508 | G. Jin et al. | Enhancing feature discrimination with pseudo-labels for foundation model in segmentation of 3D medical images (paper) | Code |
| 202508 | Y. Yang et al. | MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation (paper) | None |
| 202508 | G. Zheng et al. | Enhancing Segment Anything Model with spatial context and textural detail for cardiac MRI segmentation (paper) | Code |
| 202508 | Z. Wu et al. | Multi-Sequence Parotid Gland Lesion Segmentation via Expert Text-Guided Segment Anything Model (paper) | None |
| 202508 | R. Bhayana et al. | Segment Anything in the Ovary: Toward Scalable AI-assisted Lesion Classification (paper) | None |
| 202508 | Y. Wu et al. | SAMPO: Visual Preference Optimization for Intent-Aware Segmentation with Vision Foundation Models (paper) | None |
| 202508 | A. Roddan et al. | SAMSA 2.0: Prompting Segment Anything with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation (paper) | None |
| 202507 | A. Roy et al. | Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation? (paper) | Code |
| 202507 | H. Wang et al. | SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images (paper) | Code |
| 202507 | H. Zhuo et al. | Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation (paper) | None |
| 202507 | W. Zhou et al. | KD-MedSAM: Lightweight Knowledge Distillation of Segment Anything Model for Multi-modality Medical Image Segmentation (paper) | None |
| 202507 | C. Guo et al. | ZAP-2.5DSAM: zero additional parameters advancing 2.5D SAM adaptation to 3D tumor segmentation (paper) | Code |
| 202507 | Z. Yan et al. | SAMed-2: Selective Memory Enhanced Medical Segment Anything Model (paper) | Code |
| 202507 | T. Tang et al. | Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation (paper) | None |
| 202507 | S. Zhu et al. | Visual prompt-driven universal model for medical image segmentation in radiotherapy (paper) | None |
| 202507 | A. Zami et al. | Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays (paper) | None |
| 202507 | Y. Sui et al. | Cross-domain subcortical brain structure segmentation algorithm based on low-rank adaptation fine-tuning SAM (paper) | None |
| 202507 | A. Wang et al. | PedSemiSeg: Pedagogy-inspired semi-supervised polyp segmentation (paper) | None |
| 202506 | P. Tian et al. | MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation (paper) | None |
| 202506 | S. Sobhan et al. | MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification (paper) | None |
| 202506 | X. Han et al. | Improving a segment anything model for segmenting low-quality medical images via an adapter (paper) | None |
| 202506 | Y. Zhang et al. | Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images (paper) | Code |
| 202506 | Q. Liang et al. | STAR: Empowering Semi-Supervised Medical Image Segmentation with SAM-based Teacher-Student Architecture and Contrastive Consistency Regularization (paper) | None |
| 202506 | Y. Huang et al. | MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models (paper) | None |
| 202506 | W. Shi et al. | SIT-SAM: A semantic-integration transformer that adapts the Segment Anything Model to zero-shot medical image semantic segmentation (paper) | Code |
| 202506 | Y. He et al. | VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging (paper) | Code |
| 202506 | S. Chang et al. | Unified Medical Lesion Segmentation via Self-referring Indicator (paper) | None |
| 202506 | J. Wu et al. | SAM-aware Test-time Adaptation for Universal Medical Image Segmentation (paper) | None |
| 202505 | N. Saito et al. | Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation (paper) | None |
| 202505 | M. Colussi et al. | MIAS-SAM: Medical Image Anomaly Segmentation without thresholding (paper) | Code |
| 202505 | QH. Trinh et al. | PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging (paper) | None |
| 202505 | Q. Pan et al. | AMVLM: Alignment-Multiplicity Aware Vision-Language Model for Semi-Supervised Medical Image Segmentation (paper) | Code |
| 202505 | W. Zhou et al. | MASG-SAM: Enhancing Few-Shot Medical Image Segmentation with Multi-Scale Attention and Semantic Guidance (paper) | Code |
| 202505 | SJ. Simons et al. | SpineFM: Leveraging Foundation Models for Automatic Spine X-Ray Segmentation (paper) | None |
| 202505 | S. Sengupta et al. | SynthFM: Training Modality-Agnostic Foundation Models for Medical Image Segmentation Without Real Medical Data (paper) | None |
| 202505 | T. Ward et al. | Annotation-Efficient Task Guidance for Medical Segment Anything (paper) | Code |
| 202505 | M. Wang et al. | Efficient Fine-Tuning of SAM for Interactive Medical Image Multi-Organ Segmentation (paper) | None |
| 202505 | Y. Yao et al. | DASAM: Medical Domain Adaptation of Segment Anything Model Without Further Pre-Training (paper) | None |
| 202505 | M. Nouman et al. | Evaluating Segmentation Accuracy with Diverse Prompt Strategies in Medsam (paper) | None |
| 202505 | J. Zhang et al. | ASAM: Anatomy-Encoded Segment Anything Model for Medical Images (paper) | None |
| 202505 | J. Lyu et al. | VSS-SAM: Visual State Space-Enhanced SAM for 3D Medical Image Segmentation (paper) | None |
| 202505 | D. Lee et al. | SAM3X: Efficient 3D-Aware Network for Medical Image Segmentation Using SAM (paper) | Code |
| 202505 | AM. Rickmann et al. | Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation (paper) | None |
| 202505 | S. Liu et al. | Multi-scale feature fusion based SAM for high-quality few-shot medical image segmentation (paper) | Code |
| 202505 | H. Wang et al. | BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation (paper) | None |
| 202505 | Y. Jiang et al. | Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models (paper) | Code |
| 202505 | Z. Wu et al. | Integrating SAM priors with U-Net for enhanced multiclass cell detection in digital pathology (paper) | None |
| 202505 | T. Li et al. | TP-SA3M: text prompts-assisted SAM for myopic maculopathy segmentation (paper) | None |
| 202505 | Z. Chen et al. | UN-SAM: Domain-adaptive self-prompt segmentation for universal nuclei images (paper) | Code |
| 202504 | U. Shah et al. | SAM4EM:Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks (paper) | Code |
| 202504 | J. Khlaut et al. | RadSAM: Segmenting 3D radiological images with a 2D promptable model (paper) | None |
| 202504 | J. Wang et al. | SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation (paper) | None |
| 202504 | T. Piater et al. | Prompt-Tuning SAM: From Generalist to Specialist with only 2,048 Parameters and 16 Training Images (paper) | None |
| 202504 | X. Zhou et al. | MIT-SAM: Medical Image-Text SAM with Mutually Enhanced Heterogeneous Features Fusion for Medical Image Segmentation (paper) | Code |
| 202504 | X. Yan et al. | ICA-SAMv7: Internal carotid artery segmentation with coarse to fine network (paper) | Code |
| 202504 | L. Yu et al. | BUS-M2AE: Multi-scale Masked Autoencoder for Breast Ultrasound Image Analysis (paper) | None |
| 202504 | N. Zhang et al. | A prediction method for radiation proctitis based on SAM-Med2D model (paper) | None |
| 202504 | Y. Wang et al. | SAMBV: A Fine-tuned SAM with Interpolation Consistency Regularization for Semi-supervised Bi-ventricle Segmentation from Cardiac MRI (paper) | None |
| 202504 | J. Wei et al. | Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAMfor Generalizable Medical Segmentation (paper) | None |
| 202504 | Y. Wang et al. | Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists (paper) | Code |
| 202503 | S. Chattopadhyay et al. | Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study (paper) | None |
| 202503 | S. Xu et al. | BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection between Point and Text Prompts (paper) | None |
| 202503 | Y. Gao et al. | Show and Segment: Universal Medical Image Segmentation via In-Context Learning (paper) | None |
| 202503 | B. Li et al. | Optimization of MedSAM model based on bounding box adaptive perturbation algorithm (paper) | None |
| 202503 | Q. Ma et al. | Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation (paper) | Code |
| 202503 | B. Cui et al. | Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras (paper) | None |
| 202503 | F. Isensee et al. | nnInteractive: Redefining 3D Promptable Segmentation (paper) | Code |
| 202503 | X. Liu et al. | Segment Any Tissue: One-shot reference guided training-free automatic point prompting for medical image segmentation (paper) | Code |
| 202503 | T. Huang et al. | On-the-Fly Improving Segment Anything for Medical Image Segmentation using Auxiliary Online Learning (paper) | Code |
| 202502 | Y. Zhang et al. | SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models (paper) | Code |
| 202502 | F. Tian et al. | SAM-MedUS: a foundational model for universal ultrasound image segmentation (paper) | None |
| 202502 | A. Iltaf et al. | VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with LoRA and Atrous Attention (paper) | Code |
| 202502 | Y. Zhang et al. | SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images (paper) | Code |
| 202502 | P. Huang et al. | Diffusion-empowered AutoPrompt MedSAM (paper) | Code |
| 202502 | B. Xie et al. | Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation (paper) | None |
| 202502 | Y. Shen et al. | ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes (paper) | None |
| 202502 | B. Xie et al. | Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation (paper) | None |
| 202501 | X. He et al. | Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation (paper) | None |
| 202501 | D. Tan et al. | Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot (paper) | None |
| 202501 | Z. Yan et al. | PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation (paper) | None |
| 202501 | J. Hu et al. | SPA: Leveraging the SAM with Spatial Priors Adapter for Enhanced Medical Image Segmentation (paper) | None |
| 202412 | Y. Zhang et al. | SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization (paper) | Code |
| 202412 | D. Fan et al. | MA-SAM: A Multi-atlas Guided SAM Using Pseudo Mask Prompts without Manual Annotation for Spine Image Segmentation (paper) | Code |
| 202412 | Y. Wu et al. | Trans-SAM: Transfer Segment Anything Model to medical image segmentation with Parameter-Efficient Fine-Tuning (paper) | Code |
| 202412 | F. Zhong et al. | MEAT-SAM: More Efficient Automated Tongue Segmentation Model (paper) | None |
| 202412 | HE. Wong et al. | MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance (paper) | Code |
| 202412 | X. Shao et al. | Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer (paper) | Code |
| 202412 | K. Huang et al. | Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation (paper) | Code |
| 202412 | S. Huang et al. | SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation (paper) | None |
| 202412 | T. Ward et al. | Annotation-Efficient Task Guidance for Medical Segment Anything (paper) | Code |
| 202412 | Y. Luo et al. | Med-FastSAM: Improving Transfer Efficiency of SAM to Domain-Generalised Medical Image Segmentation (paper) | Code |
| 202412 | X. Gao et al. | RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation (paper) | None |
| 202412 | Y. Luo et al. | BiASAM: Bidirectional-attention guided Segment Anything Model for Very Few-shot Medical Image Segmentation (paper) | Code |
| 202412 | J. Hu et al. | EchoONE: Segmenting Multiple echocardiography Planes in One Model (paper) | Code |
| 202411 | B. Wittmann et al. | vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation (paper) | Code |
| 202411 | J. Xu et al. | SAM-MPA:Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting (paper) | None |
| 202411 | H. Yoon et al. | Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain (paper) | None |
| 202411 | G. Xu et al. | A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging (paper) | Code |
| 202411 | Y. Fu et al. | CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation (paper) | None |
| 202411 | J. Huo et al. | SAM-I2I: Unleash The Power of Segment Anything Model for Medical Image Trnslation (paper) | None |
| 202411 | R. Keuth et al. | SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation (paper) | Code |
| 202411 | W. Shi et al. | Segment anything model for few-shot medical image segmentation with domain tuning (paper) | None |
| 202410 | X. Ouyang et al. | Towards a general computed tomography image segmentation model for anatomical structures and lesions (paper) | None |
| 202410 | Y. Li et al. | Plug-and-play segment anything model improves nnUNet performance (paper) | Code |
| 202410 | J. Wei et al. | SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection (paper) | Code |
| 202410 | Y. Wen et al. | Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy (paper) | None |
| 202410 | C. Qin et al. | DB-SAM: Delving into High Quality Universal Medical Image Segmentation (paper) | Code |
| 202410 | Z. Wei et al. | Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation (paper) | Code |
| 202410 | Z. Xu et al. | FM-ABS: Promptable Foundation Model Drives Active Barely Supervised Learning for 3D Medical Image Segmentation (paper) | None |
| 202410 | Y. Liu et al. | FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation (paper) | Code |
| 202410 | F. Lyu et al. | Superpixel-Guided Segment Anything Model for Liver Tumor Segmentation with Couinaud Segment Prompt (paper) | None |
| 202410 | H. Shi et al. | Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation (paper) | Code |
| 202410 | Y. Huang et al. | Optimizing Efficiency and Effectiveness in Sequential Prompt Strategy for SAM Using Reinforcement Learning (paper) | None |
| 202410 | W. Li et al. | TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM (paper) | Code |
| 202410 | X. Lin et al. | Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting (paper) | Code |
| 202410 | Y. Zhao et al. | CryoSAM: Training-Free CryoET Tomogram Segmentation with Foundation Models (paper) | Code |
| 202410 | Q. Liu et al. | Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images (paper) | None |
| 202410 | I. Häkkinen et al. | Medical Image Segmentation with SAM-generated Annotations (paper) | None |
| 202409 | M. Gaillochet et al. | Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision (paper) | Code |
| 202409 | T. Koleilat et al. | MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation (paper) | Code |
| 202409 | A. Senbi et al. | Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images (paper) | Code |
| 202409 | G. Huang et al. | MCICSAM: Monte Carlo-guided Interpolation Consistency Segment Anything Model for Semi-Supervised Prostate Zone Segmentation (paper) | None |
| 202409 | H. Wang et al. | Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images (paper) | Code |
| 202409 | AS. Wahd et al. | Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts (paper) | Code |
| 202409 | Y. Liu et al. | When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels (paper) | Code |
| 202409 | X. Zheng et al. | Curriculum Prompting Foundation Models for Medical Image Segmentation (paper) | Code |
| 202408 | S. Kato et al. | Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes (paper) | Code |
| 202408 | C. Zhou et al. | SAM-SP: Self-Prompting Makes SAM Great Again (paper) | None |
| 202408 | S. Yang et al. | SAM-UNet: Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images (paper) | Code |
| 202408 | J. Wei et al. | SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection (paper) | Code |
| 202408 | X. Wei et al. | PromptSAM+: Malware Detection based on Prompt Segment Anything Model (paper) | Code |
| 202407 | J. Cai et al. | PESAM: Privacy-Enhanced Segment Anything Model for Medical Image Segmentation (paper) | None |
| 202407 | M. Asokan et al. | A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation (paper) | Code |
| 202407 | SN. Gowda et al. | CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation(paper) | None |
| 202407 | X. Huo et al. | Dr-SAM: U-Shape Structure Segment Anything Model for Generalizable Medical Image Segmentation (paper) | None |
| 202407 | H. Fang et al. | SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification (paper) | None |
| 202407 | Q. Xu et al. | ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation (paper) | Code |
| 202407 | X. Zhao et al. | SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching (paper) | None |
| 202407 | Q. Xu et al. | ProtoSAM: One Shot Medical Image Segmentation With Foundational Models (paper) | Code |
| 202407 | A. Murali et al. | CycleSAM: One-Shot Surgical Scene Segmentation using Cycle-Consistent Feature Matching to Prompt SAM (paper) | None |
| 202407 | T. Song et al. | TinySAM-Med3D: A Lightweight Segment Anything Model for Volumetric Medical Imaging with Mixture of Experts (paper) | None |
| 202407 | Y. Gao et al. | MBA-Net: SAM-driven Bidirectional Aggregation Network for Ovarian Tumor Segmentation (paper) | None |
| 202407 | J. Miao et al. | Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation (paper) | Code |
| 202407 | G. Wang et al. | SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation (paper) | None |
| 202407 | Z. Zhang et al. | Quantification of cardiac capillarization in basement-membrane-immunostained myocardial slices using Segment Anything Model (paper) | None |
| 202407 | H. Li et al. | ASPS: Augmented Segment Anything Model for Polyp Segmentation (paper) | Code |
| 202406 | Y. Xie et al. | SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues (paper) | None |
| 202406 | X. Deng et al. | MemSAM: Taming Segment Anything Model for Echocardiography Video Segmentation (paper) | Code |
| 202406 | Y. Gao | Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation (paper) | Code |
| 202406 | CD. Albelda et al. | How SAM Perceives Different mp-MRI Brain Tumor Domains? (paper) | Code |
| 202406 | T. Huang et al. | Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation (paper) | Code |
| 202406 | B. Towle et al. | SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction (paper) | Code |
| 202405 | Y. Gu et al. | LeSAM: Adapt Segment Anything Model for medical lesion segmentation (paper) | None |
| 202405 | J. Leng et al. | Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video (paper) | None |
| 202405 | MM. Rahman et al. | PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation (paper) | Code |
| 202405 | X. Zhang et al. | A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts (paper) | Code |
| 202405 | TJ. Chan et al. | SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model (paper) | None |
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Snapshot 2026-08-04 16:17:00 UTC · version 1
Segment Anything Model (SAM) uses vision transformer-based image encoder to extract image features and compute an image embedding, and prompt encoder to embed prompts and incorporate user interactions. Then extranted information from two encoders are combined to alightweight mask. Use it to navigate the topic and choose relevant methods, papers or tools.
Segment Anything Model (SAM) uses vision transformer-based image encoder to extract image features and compute an image embedding, and prompt encoder to embed prompts and incorporate user interactions. Then extranted information from two encoders are combined to alightweight mask. Use it to navigate the topic and choose relevant methods, papers or tools.
Editorial note: curated source snapshot published by Collider.club under the MIT License. Source attribution is preserved in the front matter.
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The introduction of the Segment Anything Model (SAM) (paper) and subsequent SAM2 (paper) & SAM3 (paper) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image/video segmentation, introducing a plethora of previously unexplored capabilities.
This repo will continue to track and summarize the latest research progress of SAM & Foundation Models in medical image segmentation to support ongoing research endeavors. If you find this project helpful, please consider stars or citing. Feel free to contact for any suggestions. If you would like to contribute, please open an issue.
[24.1] We provide a comprehensive survey of recent endeavors aimed at extending the efficacy of SAM to medical image segmentation tasks, encompassing both empirical benchmarking and methodological adaptations. Additionally, we explore potential avenues for future research directions in SAM's role within medical image segmentation. Please refer to the paper (CIBM'24) for more details.
[26.4] Our latest survey on the development, adaptation, and application of generalist segmentation foundation models in biomedical image and video analysis is published online at (IRAD'26).
@article{SAM4MIS,
title={Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions},
author={Zhang, Yichi and Shen, Zhenrong and Jiao, Rushi},
journal={Computers in Biology and Medicine},
volume={171},
pages={108238},
year={2024}
}
@article{zhang2026gsfm,
title={Unleashing the Potential of Generalist Segmentation Foundation Models for Biomedical Image and Video Analysis},
author={Zhang, Yichi and Shen, Zhenrong and Li, Lanlan and Zhang, Wenbo and Xue, Le},
journal={iRadiology},
year={2026}
}
Segment Anything Model (SAM) uses vision transformer-based image encoder to extract image features and compute an image embedding, and prompt encoder to embed prompts and incorporate user interactions. Then extranted information from two encoders are combined to alightweight mask decoder to generate segmentation results based on the image embedding, prompt embedding, and output token. For more details, please refer to the original paper of SAM.
A brief chronology of Segment Anything Model (SAM) and its variants for medical image segmentation in 2023.
| Date | Authors | Title | Code |
|---|---|---|---|
| 202602 | R. Zhai et al. | SAM2-driven dual-teacher framework using hierarchical cross-slice context for semi-supervised 3D medical image segmentation (paper) | None |
| 202511 | W. Su et al. | Zero-Shot Capillary Segmentation in Dermoscopy Images via SAM2: A Case Study on Oral Mucosa (paper) | None |
| 202511 | H. Buyukpatpat et al. | A Comparative Evaluation of Zero-Shot Performance of SAM, SAM2, MedSAM, and MedSAM2 Models on Lung Segmentation (paper) | None |
| 202511 | S. Chakrabarty et al. | Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data (paper) | None |
| 202511 | A. Liu et al. | MedSAM3: Delving into Segment Anything with Medical Concepts (paper) | Code |
| 202511 | M. Shokri et al. | Zero-shot Stroke Lesion Segmentation via CAM-guided Prompting of MedSAM2 (paper) | None |
| 202511 | X. Yao et al. | Towards Better Ultrasound Video Segmentation Foundation Model: An Empirical study on SAM2 Finetuning from Data Perspective (paper) | None |
| 202510 | L. Guo et al. | ESAM2-BLS: Enhanced Segment Anything Model 2 for Efficient Breast Lesion Segmentation in Ultrasound Imaging (paper) | None |
| 202508 | M. Fernandez et al. | SAM 2-Driven Self-Training for Mammogram Segmentation: Zero-Shot Mask Generation Via Pseudo-Video (paper) | Code |
| 202508 | Y. Chen et al. | SAM2Med3D: Leveraging video foundation models for 3D breast MRI segmentation (paper) | None |
| 202508 | Z. Wu et al. | Vessel-SAM2: Adapting Segment Anything 2 for Patch-Free Retinal Vessel Segmentation in Ultra-High Resolution Fundus Images (paper) | None |
| 202508 | J. He et al. | Training-Free Breast Ultrasound Image Segmentation with Retrieval-based SAM2 (paper) | None |
| 202507 | C. Wang et al. | FreqSAM2-UNet: Adapter Fine-Tuning Frequency-Aware Network of SAM2 for Universal Medical Segmentation (paper) | None |
| 202507 | G. Xu et al. | Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2 (paper) | Code |
| 202507 | E. Chukwujindu et al. | Improving Medical Image Segmentation with SAM2: Analyzing the Impact of Object Characteristics and Finetuning on Multi-Planar Datasets (paper) | Code |
| 202507 | B. Podvin et al. | SAMUSA: Segment Anything Model 2 for UltraSound Annotation (paper) | None |
| 202506 | X. Yu et al. | CRISP-SAM2 : SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation (paper) | Code |
| 202505 | M. Mansoori et al. | Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models (paper) | Code |
| 202505 | G. Huo et al. | SAMba-UNet: Synergizing SAM2 and Mamba in UNet with Heterogeneous Aggregation for Cardiac MRI Segmentation (paper) | None |
| 202504 | Y. Chen et al. | Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2 (paper) | None |
| 202504 | Y. Yamagishi et al. | Using Segment Anything Model 2 for Zero-Shot 3D Segmentation of Abdominal Organs in Computed Tomography Scans to Adapt Video Tracking Capabilities for 3D Medical Imaging: Algorithm Development and Validation (paper) | None |
| 202504 | J. Ma et al. | MedSAM2: Segment Anything in 3D Medical Images and Videos (paper) | Code |
| 202504 | JD. Gutiérrez et al. | Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt (paper) | None |
| 202504 | A. Kazemi et al. | Semi-automated segmentation of magnitude images in 4D flow MR scans using segment anything model 2 (SAM 2) (paper) | None |
| 202503 | S. Wei et al. | Self-Prompting Driven SAM2 for 3D Medical Image Segmentation (paper) | None |
| 202503 | H. Zu et al. | Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching (paper) | None |
| 202502 | X. Wang et al. | Proxy Prompt: Endowing SAM & SAM2with Auto-Interactive-Prompt for Medical Segmentation (paper) | None |
| 202502 | B. Xie et al. | RFMedSAM2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 (paper) | None |
| 202501 | G. Hoyer et al. | Scalable Evaluation Framework for Foundation Models in Musculoskeletal MRI Bridging Computational Innovation with Clinical Utility (paper) | None |
| 202501 | X. He et al. | Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation (paper) | None |
| 202411 | Z. Li et al. | Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images (paper) | None |
| 202408 | M. Mansoori et al. | Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model (paper) | Code |
| 202408 | X. Chen et al. | SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2 (paper) | Code |
| 202408 | L. Zhao et al. | Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models (paper) | None |
| 202408 | Z. Yildiz et al. | SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images (paper) | Code |
| 202408 | Y. He et al. | A Short Review and Evaluation of SAM2’s Performance in 3D CT Image Segmentation (paper) | Code |
| 202408 | X. Xiong et al. | SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation (paper) | Code |
| 202408 | H. Liu et al. | Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning (paper) | Code |
| 202408 | Y. Yamagishi et al. | Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging (paper) | None |
| 202408 | M. Mansoori et al. | Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection (paper) | Code |
| 202408 | AS. Yu et al. | Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2 (paper) | None |
| 202408 | J. Yu et al. | SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation (paper) | None |
| 202408 | T. Chen et al. | SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More (paper) | None |
| 202408 | S. Sengupta et al. | Is SAM 2 Better than SAM in Medical Image Segmentation? (paper) | None |
| 202408 | Y. Shen et al. | Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation (paper) | None |
| 202408 | M. Zhang et al. | SAM2-PATH: A better segment anything model for semantic segmentation in digital pathology (paper) | Code |
| 202408 | J. Ma et al. | Segment Anything in Medical Images and Videos: Benchmark and Deployment (paper) | Code |
| 202408 | Z. Yan et al. | Biomedical SAM 2: Segment Anything in Biomedical Images and Videos (paper) | Code |
| 202408 | C. Shen et al. | Interactive 3D Medical Image Segmentation with SAM 2 (paper) | Code |
| 202408 | A. Lou et al. | Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2 (paper) | Code |
| 202408 | J. Zhu et al. | Medical SAM 2: Segment medical images as video via Segment Anything Model 2 (paper) | Code |
| 202408 | H. Dong et al. | Segment anything model 2: an application to 2D and 3D medical images (paper) | None |
| Date | Authors | Title | Code |
|---|---|---|---|
| 202604 | Y. Zhang et al. | SemiSAM-O1: How far can we push the boundary of annotation-efficient medical image segmentation? (paper) | Code |
| 202604 | S. Huang et al. | PromptReg: Interactive Registration by “Corresponding Prompts” for Segment Anything Model (SAM) (paper) | None |
| 202604 | J. Peng et al. | MedP-CLIP: Medical CLIP with Region-Aware Prompt Integration (paper) | None |
| 202603 | T. Tang et al. | HATSAM: hierarchical adaptation strategy for segment anything model in medical imaging (paper) | None |
| 202603 | J. Hasan et al. | FM-Adapt: Foundation model adaptation with photoacoustic-supervised learning for interventional ultrasound (paper) | Code |
| 202603 | K. Borst et al. | Are General-Purpose Vision Models All We Need for 2D Medical Image Segmentation? A Cross-Dataset Empirical Study (paper) | Code |
| 202603 | K. Phuntsho et al. | Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs (paper) | None |
| 202603 | J. Tang et al. | Distillation-SAM: Knowledge Distillation Based Auto-prompt Embedding Learning for Surgical Image Segmentation (paper) | None |
| 202603 | C. Magg et al. | Prompting with the human-touch: evaluating model-sensitivity of foundation models for musculoskeletal CT segmentation (paper) | None |
| 202602 | C. Krishnan et al. | SynSAM: a hybrid synchronous learning framework with knowledge retention for prostate zonal segmentation leveraging the segment anything model (paper) | None |
| 202602 | R. Ge et al. | Generative data-engine foundation model for universal few-shot 2D vascular image segmentation (paper) | Code |
| 202602 | M. Liu et al. | StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT (paper) | None |
| 202602 | J. Miao et al. | SAM-driven cross prompting with adaptive sampling consistency for semi-supervised medical image segmentation (paper) | Code |
| 202601 | J. Li et al. | Semi-MedSAM: Adapting SAM-assisted semi-supervised multi-modality learning for medical endoscopic image segmentation (paper) | None |
| 202601 | S. Chattopadhyay et al. | On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Prompts (paper) | Code |
| 202601 | S. Tang et al. | TA-MedSAM: Text-augmented improved MedSAM for pulmonary lesion segmentation (paper) | None |
| 202601 | J. Zhu et al. | AutoPromptSeg: Automated Decoupling of Uncertainty Prompts with SAM for semi-supervised medical image segmentation (paper) | None |
| 202601 | S. Ahn et al. | Adaptability of Vision Foundation Models for 3D Medical Image Segmentation (paper) | None |
| 202512 | Z. Zhang et al. | Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation (paper) | None |
| 202512 | M. Marks et al. | CellSAM: a foundation model for cell segmentation (paper) | Code |
| 202511 | M. Rokuss et al. | VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation (paper) | Code |
| 202511 | Q. Tong et al. | MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images (paper) | Code |
| 202511 | T. Shaharabany et al. | WS-SAM: Segment Anything with Sparse Prompts for Weakly-Supervised Medical Segmentation (paper) | None |
| 202511 | FP. Salanitri et al. | SAM-guided prompt learning for Multiple Sclerosis lesion segmentation (paper) | None |
| 202511 | T. Jiang et al. | UltraSAM: A foundational medical ultrasound segmentation model with limited training data (paper) | None |
| 202511 | C. Xu et al. | Boundary-Aware Test-Time Adaptation for Zero-Shot Medical Image Segmentation (paper) | Code |
| 202511 | K. Moore et al. | Not Quite Anything: Overcoming SAM’s Limitations for 3D Medical Imaging (paper) | None |
| 202511 | Y. Zhang et al. | Continual Alignment for SAM: Rethinking Foundation Models for Medical Image Segmentation in Continual Learning (paper) | Code |
| 202510 | Y. Zhang et al. | SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images (paper) | Code |
| 202510 | J. Hu et al. | Neuroverse3D: Developing In-Context Learning Universal Model for Neuroimaging in 3D (paper) | Code |
| 202510 | S. Chen et al. | SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images (paper) | Code |
| 202510 | Y. Zhang et al. | Enhancing the Reliability of Auto-Prompting SAM for Medical Image Segmentation with Uncertainty Estimation and Rectification (paper) | None |
| 202510 | AK. Shibu et al. | MedSAM-Guided Curriculum Learning for White Matter Tract Segmentation in Block Face Imaging of Fetal Brain (paper) | None |
| 202510 | M. Attari et al. | SAM-SPJunc: Self-Prompting for Junction Detection in Retinal Images via Radius-Based Representations (paper) | None |
| 202510 | H. Chi et al. | Cross-Hierarchical Decoding with SAM for Semi-Supervised Medical Image Segmentation (paper) | None |
| 202510 | Y. Yang et al. | FM2: Fusing multiple foundation models for pathology image analysis via disentangled consensus-divergence representation (paper) | None |
| 202510 | Q. Xu et al. | De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation (paper) | Code |
| 202510 | Y. Yang et al. | SAM2-3dMed: Empowering SAM2 for 3D Medical Image Segmentation (paper) | None |
| 202510 | TC. Ndir et al. | Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training (paper) | None |
| 202509 | G. Zhang et al. | IPLC+: SAM-Guided Iterative Pseudo Label Correction for Source-Free Domain Adaptation in Medical Image Segmentation (paper) | Code |
| 202509 | Z. Liu et al. | BALR-SAM: Boundary-Aware Low-Rank Adaptation of SAM for Resource-Efficient Medical Image Segmentation (paper) | None |
| 202509 | J. Zhuang et al. | Bio2Vol: Adapting 2D Biomedical Foundation Models for Volumetric Medical Image Segmentation (paper) | Code |
| 202509 | Z. Peng et al. | HA-SAM: Hierarchically Adapting SAM for Nerve Segmentation in Ultrasound Images (paper) | None |
| 202509 | Y. Wang et al. | Collect vascular specimens in one cabinet: A hierarchical prompt-guided universal model for 3D vascular segmentation (paper) | Code |
| 202509 | Y. Zhang et al. | Embedded framework for clinical medical image segment anything in resource limited healthcare regions (paper) | Code |
| 202509 | L. Li et al. | Segment Anything Model for Gastric Cancer (paper) | None |
| 202509 | Z. Tu et al. | Spatial-Temporal Memory Filtering SAM for Lesion Segmentation in Breast Ultrasound Videos (paper) | Code |
| 202509 | R. Li et al. | MoE-SAM: Enhancing SAM for Medical Image Segmentation with Mixture-of-Experts (paper) | Code |
| 202509 | Q. Li et al. | From Generalist to Specialist: Distilling a Mixture of Foundation Models for Domain-Specific Medical Image Segmentation (paper) | None |
| 202509 | M. Zhang et al. | Towards Robust Retinal Vessel Segmentation via Reducing Open-Set Label Noises from SAM-Generated Masks (paper) | None |
| 202509 | H. Su et al. | Sparsely Annotated Medical Image Segmentation via Cross-SAM of 3D and 2D Networks (paper) | Code |
| 202509 | T. Wang et al. | pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation (paper) | None |
| 202509 | C. Qin et al. | DUR-Net+: Semi-Supervised Abdominal CT Pheochromocytoma Segmentation Via Dynamic Uncertainty Rectified and Prior Knowledge From SAM-Med3D (paper) | None |
| 202509 | D. Chen et al. | SAMTNU: Adaptive Segment Anything Model for Thyroid and Nodule Ultrasound Image Segmentation (paper) | None |
| 202509 | X. Zhang et al. | Leveraging Multi-Text Joint Prompts in SAM for Robust Medical Image Segmentation (paper) | None |
| 202509 | T. Ward et al. | A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation (paper) | Code |
| 202509 | X. Yu et al. | Medical SAM-Clip Grafting for brain tumor segmentation (paper) | None |
| 202508 | S. Zhang et al. | A generalist foundation model and database for open-world medical image segmentation (paper) | Code |
| 202508 | Z. Zhao et al. | Large-vocabulary segmentation for medical images with text prompts (paper) | Code |
| 202508 | AA. Shami et al. | Persistent Homology and Segment Anything Model for Automated Zero-Shot Localized Medical X-ray Images Segmentation (PH-SAM) (paper) | None |
| 202508 | B. Huang et al. | E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation (paper) | Code |
| 202508 | X. Sun et al. | MFB-SAC: A Multi-Scale Frequency and Boundary-Enhanced SAM for Cell Segmentation (paper) | Code |
| 202508 | G. Jin et al. | Enhancing feature discrimination with pseudo-labels for foundation model in segmentation of 3D medical images (paper) | Code |
| 202508 | Y. Yang et al. | MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation (paper) | None |
| 202508 | G. Zheng et al. | Enhancing Segment Anything Model with spatial context and textural detail for cardiac MRI segmentation (paper) | Code |
| 202508 | Z. Wu et al. | Multi-Sequence Parotid Gland Lesion Segmentation via Expert Text-Guided Segment Anything Model (paper) | None |
| 202508 | R. Bhayana et al. | Segment Anything in the Ovary: Toward Scalable AI-assisted Lesion Classification (paper) | None |
| 202508 | Y. Wu et al. | SAMPO: Visual Preference Optimization for Intent-Aware Segmentation with Vision Foundation Models (paper) | None |
| 202508 | A. Roddan et al. | SAMSA 2.0: Prompting Segment Anything with Spectral Angles for Hyperspectral Interactive Medical Image Segmentation (paper) | None |
| 202507 | A. Roy et al. | Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation? (paper) | Code |
| 202507 | H. Wang et al. | SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images (paper) | Code |
| 202507 | H. Zhuo et al. | Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation (paper) | None |
| 202507 | W. Zhou et al. | KD-MedSAM: Lightweight Knowledge Distillation of Segment Anything Model for Multi-modality Medical Image Segmentation (paper) | None |
| 202507 | C. Guo et al. | ZAP-2.5DSAM: zero additional parameters advancing 2.5D SAM adaptation to 3D tumor segmentation (paper) | Code |
| 202507 | Z. Yan et al. | SAMed-2: Selective Memory Enhanced Medical Segment Anything Model (paper) | Code |
| 202507 | T. Tang et al. | Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation (paper) | None |
| 202507 | S. Zhu et al. | Visual prompt-driven universal model for medical image segmentation in radiotherapy (paper) | None |
| 202507 | A. Zami et al. | Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays (paper) | None |
| 202507 | Y. Sui et al. | Cross-domain subcortical brain structure segmentation algorithm based on low-rank adaptation fine-tuning SAM (paper) | None |
| 202507 | A. Wang et al. | PedSemiSeg: Pedagogy-inspired semi-supervised polyp segmentation (paper) | None |
| 202506 | P. Tian et al. | MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation (paper) | None |
| 202506 | S. Sobhan et al. | MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification (paper) | None |
| 202506 | X. Han et al. | Improving a segment anything model for segmenting low-quality medical images via an adapter (paper) | None |
| 202506 | Y. Zhang et al. | Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images (paper) | Code |
| 202506 | Q. Liang et al. | STAR: Empowering Semi-Supervised Medical Image Segmentation with SAM-based Teacher-Student Architecture and Contrastive Consistency Regularization (paper) | None |
| 202506 | Y. Huang et al. | MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models (paper) | None |
| 202506 | W. Shi et al. | SIT-SAM: A semantic-integration transformer that adapts the Segment Anything Model to zero-shot medical image semantic segmentation (paper) | Code |
| 202506 | Y. He et al. | VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging (paper) | Code |
| 202506 | S. Chang et al. | Unified Medical Lesion Segmentation via Self-referring Indicator (paper) | None |
| 202506 | J. Wu et al. | SAM-aware Test-time Adaptation for Universal Medical Image Segmentation (paper) | None |
| 202505 | N. Saito et al. | Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation (paper) | None |
| 202505 | M. Colussi et al. | MIAS-SAM: Medical Image Anomaly Segmentation without thresholding (paper) | Code |
| 202505 | QH. Trinh et al. | PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging (paper) | None |
| 202505 | Q. Pan et al. | AMVLM: Alignment-Multiplicity Aware Vision-Language Model for Semi-Supervised Medical Image Segmentation (paper) | Code |
| 202505 | W. Zhou et al. | MASG-SAM: Enhancing Few-Shot Medical Image Segmentation with Multi-Scale Attention and Semantic Guidance (paper) | Code |
| 202505 | SJ. Simons et al. | SpineFM: Leveraging Foundation Models for Automatic Spine X-Ray Segmentation (paper) | None |
| 202505 | S. Sengupta et al. | SynthFM: Training Modality-Agnostic Foundation Models for Medical Image Segmentation Without Real Medical Data (paper) | None |
| 202505 | T. Ward et al. | Annotation-Efficient Task Guidance for Medical Segment Anything (paper) | Code |
| 202505 | M. Wang et al. | Efficient Fine-Tuning of SAM for Interactive Medical Image Multi-Organ Segmentation (paper) | None |
| 202505 | Y. Yao et al. | DASAM: Medical Domain Adaptation of Segment Anything Model Without Further Pre-Training (paper) | None |
| 202505 | M. Nouman et al. | Evaluating Segmentation Accuracy with Diverse Prompt Strategies in Medsam (paper) | None |
| 202505 | J. Zhang et al. | ASAM: Anatomy-Encoded Segment Anything Model for Medical Images (paper) | None |
| 202505 | J. Lyu et al. | VSS-SAM: Visual State Space-Enhanced SAM for 3D Medical Image Segmentation (paper) | None |
| 202505 | D. Lee et al. | SAM3X: Efficient 3D-Aware Network for Medical Image Segmentation Using SAM (paper) | Code |
| 202505 | AM. Rickmann et al. | Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation (paper) | None |
| 202505 | S. Liu et al. | Multi-scale feature fusion based SAM for high-quality few-shot medical image segmentation (paper) | Code |
| 202505 | H. Wang et al. | BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation (paper) | None |
| 202505 | Y. Jiang et al. | Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models (paper) | Code |
| 202505 | Z. Wu et al. | Integrating SAM priors with U-Net for enhanced multiclass cell detection in digital pathology (paper) | None |
| 202505 | T. Li et al. | TP-SA3M: text prompts-assisted SAM for myopic maculopathy segmentation (paper) | None |
| 202505 | Z. Chen et al. | UN-SAM: Domain-adaptive self-prompt segmentation for universal nuclei images (paper) | Code |
| 202504 | U. Shah et al. | SAM4EM:Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks (paper) | Code |
| 202504 | J. Khlaut et al. | RadSAM: Segmenting 3D radiological images with a 2D promptable model (paper) | None |
| 202504 | J. Wang et al. | SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation (paper) | None |
| 202504 | T. Piater et al. | Prompt-Tuning SAM: From Generalist to Specialist with only 2,048 Parameters and 16 Training Images (paper) | None |
| 202504 | X. Zhou et al. | MIT-SAM: Medical Image-Text SAM with Mutually Enhanced Heterogeneous Features Fusion for Medical Image Segmentation (paper) | Code |
| 202504 | X. Yan et al. | ICA-SAMv7: Internal carotid artery segmentation with coarse to fine network (paper) | Code |
| 202504 | L. Yu et al. | BUS-M2AE: Multi-scale Masked Autoencoder for Breast Ultrasound Image Analysis (paper) | None |
| 202504 | N. Zhang et al. | A prediction method for radiation proctitis based on SAM-Med2D model (paper) | None |
| 202504 | Y. Wang et al. | SAMBV: A Fine-tuned SAM with Interpolation Consistency Regularization for Semi-supervised Bi-ventricle Segmentation from Cardiac MRI (paper) | None |
| 202504 | J. Wei et al. | Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAMfor Generalizable Medical Segmentation (paper) | None |
| 202504 | Y. Wang et al. | Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists (paper) | Code |
| 202503 | S. Chattopadhyay et al. | Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study (paper) | None |
| 202503 | S. Xu et al. | BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection between Point and Text Prompts (paper) | None |
| 202503 | Y. Gao et al. | Show and Segment: Universal Medical Image Segmentation via In-Context Learning (paper) | None |
| 202503 | B. Li et al. | Optimization of MedSAM model based on bounding box adaptive perturbation algorithm (paper) | None |
| 202503 | Q. Ma et al. | Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation (paper) | Code |
| 202503 | B. Cui et al. | Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras (paper) | None |
| 202503 | F. Isensee et al. | nnInteractive: Redefining 3D Promptable Segmentation (paper) | Code |
| 202503 | X. Liu et al. | Segment Any Tissue: One-shot reference guided training-free automatic point prompting for medical image segmentation (paper) | Code |
| 202503 | T. Huang et al. | On-the-Fly Improving Segment Anything for Medical Image Segmentation using Auxiliary Online Learning (paper) | Code |
| 202502 | Y. Zhang et al. | SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models (paper) | Code |
| 202502 | F. Tian et al. | SAM-MedUS: a foundational model for universal ultrasound image segmentation (paper) | None |
| 202502 | A. Iltaf et al. | VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with LoRA and Atrous Attention (paper) | Code |
| 202502 | Y. Zhang et al. | SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images (paper) | Code |
| 202502 | P. Huang et al. | Diffusion-empowered AutoPrompt MedSAM (paper) | Code |
| 202502 | B. Xie et al. | Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation (paper) | None |
| 202502 | Y. Shen et al. | ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes (paper) | None |
| 202502 | B. Xie et al. | Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation (paper) | None |
| 202501 | X. He et al. | Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation (paper) | None |
| 202501 | D. Tan et al. | Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot (paper) | None |
| 202501 | Z. Yan et al. | PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation (paper) | None |
| 202501 | J. Hu et al. | SPA: Leveraging the SAM with Spatial Priors Adapter for Enhanced Medical Image Segmentation (paper) | None |
| 202412 | Y. Zhang et al. | SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization (paper) | Code |
| 202412 | D. Fan et al. | MA-SAM: A Multi-atlas Guided SAM Using Pseudo Mask Prompts without Manual Annotation for Spine Image Segmentation (paper) | Code |
| 202412 | Y. Wu et al. | Trans-SAM: Transfer Segment Anything Model to medical image segmentation with Parameter-Efficient Fine-Tuning (paper) | Code |
| 202412 | F. Zhong et al. | MEAT-SAM: More Efficient Automated Tongue Segmentation Model (paper) | None |
| 202412 | HE. Wong et al. | MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance (paper) | Code |
| 202412 | X. Shao et al. | Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer (paper) | Code |
| 202412 | K. Huang et al. | Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation (paper) | Code |
| 202412 | S. Huang et al. | SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation (paper) | None |
| 202412 | T. Ward et al. | Annotation-Efficient Task Guidance for Medical Segment Anything (paper) | Code |
| 202412 | Y. Luo et al. | Med-FastSAM: Improving Transfer Efficiency of SAM to Domain-Generalised Medical Image Segmentation (paper) | Code |
| 202412 | X. Gao et al. | RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation (paper) | None |
| 202412 | Y. Luo et al. | BiASAM: Bidirectional-attention guided Segment Anything Model for Very Few-shot Medical Image Segmentation (paper) | Code |
| 202412 | J. Hu et al. | EchoONE: Segmenting Multiple echocardiography Planes in One Model (paper) | Code |
| 202411 | B. Wittmann et al. | vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation (paper) | Code |
| 202411 | J. Xu et al. | SAM-MPA:Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting (paper) | None |
| 202411 | H. Yoon et al. | Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain (paper) | None |
| 202411 | G. Xu et al. | A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging (paper) | Code |
| 202411 | Y. Fu et al. | CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation (paper) | None |
| 202411 | J. Huo et al. | SAM-I2I: Unleash The Power of Segment Anything Model for Medical Image Trnslation (paper) | None |
| 202411 | R. Keuth et al. | SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation (paper) | Code |
| 202411 | W. Shi et al. | Segment anything model for few-shot medical image segmentation with domain tuning (paper) | None |
| 202410 | X. Ouyang et al. | Towards a general computed tomography image segmentation model for anatomical structures and lesions (paper) | None |
| 202410 | Y. Li et al. | Plug-and-play segment anything model improves nnUNet performance (paper) | Code |
| 202410 | J. Wei et al. | SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection (paper) | Code |
| 202410 | Y. Wen et al. | Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy (paper) | None |
| 202410 | C. Qin et al. | DB-SAM: Delving into High Quality Universal Medical Image Segmentation (paper) | Code |
| 202410 | Z. Wei et al. | Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation (paper) | Code |
| 202410 | Z. Xu et al. | FM-ABS: Promptable Foundation Model Drives Active Barely Supervised Learning for 3D Medical Image Segmentation (paper) | None |
| 202410 | Y. Liu et al. | FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation (paper) | Code |
| 202410 | F. Lyu et al. | Superpixel-Guided Segment Anything Model for Liver Tumor Segmentation with Couinaud Segment Prompt (paper) | None |
| 202410 | H. Shi et al. | Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation (paper) | Code |
| 202410 | Y. Huang et al. | Optimizing Efficiency and Effectiveness in Sequential Prompt Strategy for SAM Using Reinforcement Learning (paper) | None |
| 202410 | W. Li et al. | TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM (paper) | Code |
| 202410 | X. Lin et al. | Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting (paper) | Code |
| 202410 | Y. Zhao et al. | CryoSAM: Training-Free CryoET Tomogram Segmentation with Foundation Models (paper) | Code |
| 202410 | Q. Liu et al. | Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images (paper) | None |
| 202410 | I. Häkkinen et al. | Medical Image Segmentation with SAM-generated Annotations (paper) | None |
| 202409 | M. Gaillochet et al. | Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision (paper) | Code |
| 202409 | T. Koleilat et al. | MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation (paper) | Code |
| 202409 | A. Senbi et al. | Towards Ground-truth-free Evaluation of Any Segmentation in Medical Images (paper) | Code |
| 202409 | G. Huang et al. | MCICSAM: Monte Carlo-guided Interpolation Consistency Segment Anything Model for Semi-Supervised Prostate Zone Segmentation (paper) | None |
| 202409 | H. Wang et al. | Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images (paper) | Code |
| 202409 | AS. Wahd et al. | Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts (paper) | Code |
| 202409 | Y. Liu et al. | When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels (paper) | Code |
| 202409 | X. Zheng et al. | Curriculum Prompting Foundation Models for Medical Image Segmentation (paper) | Code |
| 202408 | S. Kato et al. | Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes (paper) | Code |
| 202408 | C. Zhou et al. | SAM-SP: Self-Prompting Makes SAM Great Again (paper) | None |
| 202408 | S. Yang et al. | SAM-UNet: Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images (paper) | Code |
| 202408 | J. Wei et al. | SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection (paper) | Code |
| 202408 | X. Wei et al. | PromptSAM+: Malware Detection based on Prompt Segment Anything Model (paper) | Code |
| 202407 | J. Cai et al. | PESAM: Privacy-Enhanced Segment Anything Model for Medical Image Segmentation (paper) | None |
| 202407 | M. Asokan et al. | A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation (paper) | Code |
| 202407 | SN. Gowda et al. | CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation(paper) | None |
| 202407 | X. Huo et al. | Dr-SAM: U-Shape Structure Segment Anything Model for Generalizable Medical Image Segmentation (paper) | None |
| 202407 | H. Fang et al. | SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification (paper) | None |
| 202407 | Q. Xu et al. | ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation (paper) | Code |
| 202407 | X. Zhao et al. | SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching (paper) | None |
| 202407 | Q. Xu et al. | ProtoSAM: One Shot Medical Image Segmentation With Foundational Models (paper) | Code |
| 202407 | A. Murali et al. | CycleSAM: One-Shot Surgical Scene Segmentation using Cycle-Consistent Feature Matching to Prompt SAM (paper) | None |
| 202407 | T. Song et al. | TinySAM-Med3D: A Lightweight Segment Anything Model for Volumetric Medical Imaging with Mixture of Experts (paper) | None |
| 202407 | Y. Gao et al. | MBA-Net: SAM-driven Bidirectional Aggregation Network for Ovarian Tumor Segmentation (paper) | None |
| 202407 | J. Miao et al. | Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation (paper) | Code |
| 202407 | G. Wang et al. | SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation (paper) | None |
| 202407 | Z. Zhang et al. | Quantification of cardiac capillarization in basement-membrane-immunostained myocardial slices using Segment Anything Model (paper) | None |
| 202407 | H. Li et al. | ASPS: Augmented Segment Anything Model for Polyp Segmentation (paper) | Code |
| 202406 | Y. Xie et al. | SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues (paper) | None |
| 202406 | X. Deng et al. | MemSAM: Taming Segment Anything Model for Echocardiography Video Segmentation (paper) | Code |
| 202406 | Y. Gao | Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation (paper) | Code |
| 202406 | CD. Albelda et al. | How SAM Perceives Different mp-MRI Brain Tumor Domains? (paper) | Code |
| 202406 | T. Huang et al. | Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation (paper) | Code |
| 202406 | B. Towle et al. | SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction (paper) | Code |
| 202405 | Y. Gu et al. | LeSAM: Adapt Segment Anything Model for medical lesion segmentation (paper) | None |
| 202405 | J. Leng et al. | Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video (paper) | None |
| 202405 | MM. Rahman et al. | PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation (paper) | Code |
| 202405 | X. Zhang et al. | A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts (paper) | Code |
| 202405 | TJ. Chan et al. | SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model (paper) | None |
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