ArticleNature methods2025
Segment Anything for Microscopy.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 112 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
112 citing papers in PubMed.
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- Pseudotime uncovers restructuring dynamics and mitigators of metal catalyst deactivation.Science advances · 2026Article
- Integrative genomics and zero-shot deep learning-based phenotyping reveal the genetic architecture of seed traits in mungbean (Vigna radiata L.).The plant genome · 2026Article
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- Disruption of the research software landscape through AI software generation.Nature methods · 2026Article
- Label-free ovarian cancer histopathological diagnosis using two-photon autofluorescence microscopy with a joint denoising and segmentation framework.Light, science & applications · 2026Article
- Negative thermal expansion and its manifestations within confined lamellar-structured concentrated surfactant solutions.Soft matter · 2026Article
- Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- OrgLine: A versatile pipeline for organoid morphometry using detector-guided prompts.Cell reports methods · 2026Article
- Identifying phenotype-genotype-function coupling in 3D organoid imaging using Shape, Appearance and Motion Phenotype Observation Tool (SPOT).Nature communications · 2026Article
- Quantitative validation of the "coastline" heuristic for café-au-lait macule borders in neurofibromatosis type 1 and McCune-Albright syndrome.Journal of the American Academy of Dermatology · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Cell size reduction distinctly scales spindle elongation and chromosome segregation in C. elegans.Nature communications · 2026Article
- Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026Article
- Deep learning-powered quantification of endocrine cells and CD3Diabetologia · 2026Article
- Deep learning for optoacoustic imaging of reversibly switchable proteins: training and performance testing using simulated multispectral optoacoustic tomography images.Journal of biomedical optics · 2026Article
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast.Npj imaging · 2026Review
52 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
19 authors.
Funding
Abstract
Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models.
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What OpenQuestion holds
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.