Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

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 independ...

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Veröffentlicht in:Nature methods Jg. 19; H. 11; S. 1438 - 1448
Hauptverfasser: Cutler, Kevin J., Stringer, Carsen, Lo, Teresa W., Rappez, Luca, Stroustrup, Nicholas, Brook Peterson, S., Wiggins, Paul A., Mougous, Joseph D.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.11.2022
Nature Publishing Group
Schlagworte:
631/1647/245
631/326/41
Algorithms
Antagonism
Antibiotics
Artificial neural networks
Bacteria
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Cytology
Image acquisition
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Life Sciences
Microscopy
Morphology
Neural networks
Optical properties
Phenotypes
Physical characteristics
Proteomics
ISSN:1548-7091, 1548-7105, 1548-7105
Online-Zugang:Volltext
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Zusammenfassung: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. Omnipose is a deep neural network algorithm for image segmentation that improves upon existing approaches by solving the challenging problem of accurately segmenting morphologically diverse cells from images acquired with any modality.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-022-01639-4