ArticleNature methods2022
Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers.
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Who cites it
179 citing papers in PubMed.
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- Artificial Intelligence-Empowered Single-Cell Phenotyping for Rapid, Automated Pathogen Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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- Time-resolved phenotyping at subcellular resolution reveals shared principles and key trade-offs across antimicrobial peptide activities.bioRxiv : the preprint server for biology · 2026Article
- Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026Article
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- A deep learning-enabled toolkit for the 3D segmentation of ventricular cardiomyocytes.The Journal of physiology · 2026Article
- Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets.Nature biotechnology · 2026Article
- Robust Cell Segmentation for Size Distribution Estimation via Synthetic-Data Training.Biotechnology journal · 2026Article
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- Phage-derived proteins and conjugates induce pilus detachment.Journal of bacteriology · 2026Article
- TRIM21 induces selective autophagy of viruses and bacteria.Molecular cell · 2026Article
119 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.
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