ArticleNature methods2025
CellSAM: a foundation model for cell segmentation.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Multimodal High-Throughput Screening Raman Spectroscopy for Label-Free Single-Cell Characterization of Recombinant Protein Production in Baculovirus Expression Vector Systems.Analytical chemistry · 2026Article
- A High-Content Imaging Pipeline to Investigate Subcytotoxic Effects in RTgill-W1 Cells.Environmental science & technology · 2026Article
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.eLife · 2026Article
- NeuroSeg-MF: robust neuron segmentation in two-photon CaBiomedical optics express · 2026Article
- Robust Cell Segmentation for Size Distribution Estimation via Synthetic-Data Training.Biotechnology journal · 2026Article
- Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching.ArXiv · 2026Article
- Review
- Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling.Communications biology · 2026Article
- OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.Research square · 2026Article
- Foundation model cascades enable zero-shot microscopy image analysis for cell therapy manufacturing.Cytotherapy · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Foundation cell segmentation models performance on live microscopy and spatial-omics data.bioRxiv : the preprint server for biology · 2026Article
- Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas.Scientific data · 2026Article
- Profiling extracellular matrix-driven heterogeneity of single cell migration and morphology.Scientific reports · 2026Article
- Cellular neighborhoods in cancer.Nature cancer · 2026Article
- An automated cell-tracking pipeline for the analysis of neutrophil dynamics.Frontiers in bioinformatics · 2026Article
- ST-FFPE-mIF: integrating spatial transcriptomics and multiplex immunofluorescence in formalin-fixed paraffin-embedded tissues using Stereo-seq.Genome biology · 2025Article
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
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Authors and funding
19 authors.
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Abstract
Cells are a fundamental unit of biological organization, and identifying them in imaging data-cell segmentation-is a critical task for various cellular imaging experiments. Although deep learning methods have led to substantial progress on this problem, most models are specialist models that work well for specific domains but cannot be applied across domains or scale well with large amounts of data. Here we present CellSAM, a universal model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells, yeast and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. Additionally, we demonstrate how CellSAM can be applied across diverse bioimage analysis workflows. A deployed version of CellSAM is available at https://cellsam.deepcell.org/ .
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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 Socratic graph.