Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

  • Authors:

  • Rehan Ali;Mark Gooding;Tünde Szilágyi;Borivoj Vojnovic;Martin Christlieb;Michael Brady

  • Affiliations:

  • Stanford University, Department of Radiation Physics, 875 Blake Wilbur Drive, CC-G206, 94305, Stanford, CA, USA;Mirada Medical Ltd, Innovation House, Mill Street, OX2 0JX, Oxford, UK;University of Oxford, Department of Engineering Science, FRS FREng FMedSci Wolfson Medical Vision Lab, Parks Road, OX1 3PJ, Oxford, UK;University of Oxford, Gray Institute for Radiation Oncology and Biology, Old Road Campus Research Building, OX3 7QD, Oxford, UK;University of Oxford, Gray Institute for Radiation Oncology and Biology, Old Road Campus Research Building, OX3 7QD, Oxford, UK;University of Oxford, Department of Engineering Science, FRS FREng FMedSci Wolfson Medical Vision Lab, Parks Road, OX1 3PJ, Oxford, UK

  • Venue:
  • Machine Vision and Applications - Special Issue: Microscopy Image Analysis for Biomedical Applications
  • Year:
  • 2012

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Abstract

The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at http://www.sephace.com, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.