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Bacterial cells identification in Differential Interference Contrast (DIC) microscopy images

Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding.

We have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides.

Bacterial cell body segmentation workflow: DIC input image, DIC shear direction estimation: an obtained orientation  a=48.20[deg] (red line) , DIC image reconstruction, an overlay of the segmented cell body in red.

Bacterial cell body segmentation workflow: DIC input image, DIC shear direction estimation: an obtained orientation a=48.20[deg] (red line), DIC image reconstruction, an overlay of the segmented cell body in red.

Collaborators:

  • Dr Mark Roberts, Oxford Centre for Integrative Systems Biology, University of Oxford, UK
  • Prof.  Judith Armitage, Department of Biochemistry, University of Oxford, UK
  • Dr Vicente Grau, Oxford e-Research Centre, University of Oxford, UK

Publications:

  • [2013,article] bibtex
    B. Obara, M. Roberts, J. Armitage, and V. Grau, "Bioimage informatics approach for bacterial cells identification in Differential Interference Contrast microscopy images," BMC Bioinformatics, vol. 14, iss. 134, 2013.
    @article{Obara2013c,
      author = {Boguslaw Obara and Mark Roberts and Judy Armitage and Vicente Grau},
      title = {Bioimage informatics approach for bacterial cells identification in {Differential Interference Contrast} microscopy images},
      journal = {BMC Bioinformatics},
      volume = {14},
      number = {134},
      year = {2013}
    }