We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Accurate morphology preserving segmentation of overlapping cells based on active contours.

Molnar, Csaba and Jermyn, Ian H. and Kato, Zoltan and Rahkama, Vesa and Östling, Päivi and Mikkonen, Piia and Pietiäinen, Vilja and Horvath, Peter (2016) 'Accurate morphology preserving segmentation of overlapping cells based on active contours.', Scientific reports., 6 . p. 32412.


The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the “gas of near circles” active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts.

Item Type:Article
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution.
Download PDF
Publisher Web site:
Publisher statement:This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
Date accepted:01 August 2016
Date deposited:19 September 2016
Date of first online publication:26 August 2016
Date first made open access:19 September 2016

Save or Share this output

Look up in GoogleScholar