Automated Cell Segmentation in Phase-Contrast Images based on Classification and Region Growing

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Authors	STOKLASA Roman BÁLEK Lukáš KREJČÍ Pavel MATULA Petr
Year of publication	2015
Type	Article in Proceedings
Conference	Proceedings of 2015 IEEE International Symposium on Biomedical Imaging, 2015.
MU Faculty or unit	Faculty of Informatics
Citation
Web	https://ieeexplore.ieee.org/document/7164149
Doi	http://dx.doi.org/10.1109/ISBI.2015.7164149
Field	Use of computers, robotics and its application
Keywords	phase-contrast microscopy; segmentation; classification; superpixel; cells
Description	Cell segmentation in phase-contrast microscopy images remains a challenging problem because of the large variability in subcellular structures and imaging artifacts. In this paper, we present an approach to the automatic segmentation of tightly packed cells in phase-contrast images. We combine the classification of superpixels with the region-growing method to locate cell membrane boundaries. We demonstrate that such a combined approach is able to perform the task of cell detection and segmentation with a high level of precision. On the presented dataset, we achieved 90% precision with 78% recall. The results indicate that this method is suitable for real biological applications.
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