ArticleIS&T International Symposium on Electronic Imaging2025
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.
Article in IS&T International Symposium on Electronic Imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- WeakMitoSAM: competitive prompt aggregation for point-supervised mitochondria segmentation in electron microscopy images.Biomedical optics express · 2026Article
- Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- Zero-Shot Vertebral Instance Segmentation on DICOM Spine Radiographs Using Promptable Segment Anything Models.Journal of clinical medicine · 2026Article
- An intelligent segmentation method for leakage points in central serous chorioretinopathy based on fluorescein angiography images.International journal of ophthalmology · 2026Article
- Explainable opportunistic osteoporosis screening from chest X-rays: a retrospective comparison of foundation models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.Biomedical engineering and computational biology · 2026Article
- PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.NPJ digital medicine · 2025Article
- CellSAM: a foundation model for cell segmentation.Nature methods · 2025Article
- Generative AI and foundation models in medical image.Radiological physics and technology · 2025Review
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
- Evaluating cell AI foundation models in kidney pathology with human-in-the-loop enrichment.Communications medicine · 2025Article
- BoxCell: Leveraging SAM for Cell Segmentation with Box Supervision.Scientific reports · 2025Article
- Sustainable Ultralightweight U-Net-Based Architecture for Myocardium Segmentation.Journal of clinical medicine · 2025Article
- TriSAM: Tri-Plane SAM for Zero-Shot Cortical Blood Vessel Segmentation in VEM Images.IEEE journal of biomedical and health informatics · 2025Article
- A fine-tuned foundational model SurgiSAM2 for surgical video anatomy segmentation and detection.Scientific reports · 2025Article
- Fine-grained multiclass nuclei segmentation with molecular empowered all-in-SAM model.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Foundational Segmentation Models and Clinical Data Mining Enable Accurate Computer Vision for Lung Cancer.Journal of imaging informatics in medicine · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation.
Identifiers
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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.