ArticleDiagnostics (Basel, Switzerland)2023
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 119 citations in OpenAlex.
- MB-SwinRefiner: Mammographic Mass Segmentation with Probability-Guided Residual Refinement.Journal of imaging · 2026Article
- Segmentation and Clustering Analysis of Scanned Foot Models for Intelligent Diabetic Foot Ulcer Offloading Boots.Journal of imaging informatics in medicine · 2026Article
- Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- Benchmarking Multimodal Vision Frontier Models With Lumbar Spine MRIs for Grading Lumbar Spinal Stenosis.Global spine journal · 2026Article
- Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- OpenVocabCT: Toward Universal Text-Driven CT Image Segmentation.IEEE transactions on medical imaging · 2026Article
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review.NMR in biomedicine · 2025Review
- SAM-Based Approach for Automated Fabric Anisotropy Quantification in Concrete Aggregates.Sensors (Basel, Switzerland) · 2025Article
- Automated Rice Seedling Segmentation and Unsupervised Health Assessment Using Segment Anything Model with Multi-Modal Feature Analysis.Sensors (Basel, Switzerland) · 2025Article
- The generative revolution: AI foundation models in geospatial health-applications, challenges and future research.International journal of health geographics · 2025Article
- Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.Chinese medical journal · 2025Review
- Deep learning for deep learning performance: How much data is needed for segmentation in biomedical imaging?PloS one · 2025Article
- Rectal cancer segmentation via HHF-SAM: a hierarchical hypercolumn-guided fusion segment anything model.Frontiers in artificial intelligence · 2025Article
- BLoss-DDNet: bending loss and dual-task decoding network for overlapping cell nucleus segmentation of cervical clinical LBC images.Frontiers in artificial intelligence · 2025Article
- Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging.Journal of imaging · 2024Article
- MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation.Medical image analysis · 2024Article
- Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis.Scientific reports · 2024Article
- Foundation models in ophthalmology.The British journal of ophthalmology · 2024Review
- Reducing Training Data Using Pre-Trained Foundation Models: A Case Study on Traffic Sign Segmentation Using the Segment Anything Model.Journal of imaging · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Medical image analysis plays an important role in clinical diagnosis. In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology. Those benchmarks are representative and commonly used in model development. Our experimental results indicate that while SAM presents remarkable segmentation performance on images from the general domain, its zero-shot segmentation ability remains restricted for out-of-distribution images, e.g., medical images. In addition, SAM exhibits inconsistent zero-shot segmentation performance across different unseen medical domains. For certain structured targets, e.g., blood vessels, the zero-shot segmentation of SAM completely failed. In contrast, a simple fine-tuning of it with a small amount of data could lead to remarkable improvement of the segmentation quality, showing the great potential and feasibility of using fine-tuned SAM to achieve accurate medical image segmentation for a precision diagnostics. Our study indicates the versatility of generalist vision foundation models on medical imaging, and their great potential to achieve desired performance through fine-turning and eventually address the challenges associated with accessing large and diverse medical datasets in support of clinical diagnostics.
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Registered trials
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.