WANG, Yuliang; WANG, C.; ZHANG, Zaicheng

doi:10.1111/jmi.12673

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WANG, Yuliang
School of Mechanical Engineering and Automation [Beijing]

WANG, C.
School of Mechanical Engineering and Automation [Beijing]

ZHANG, Zaicheng
Laboratoire Ondes et Matière d'Aquitaine [LOMA]

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Journal of Microscopy. 2018, vol. 270, n° 2, p. 188-199

Wiley

Résumé en anglais

Automated cell segmentation plays a key role in characterisa-tions of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmenta-tion. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively.< Réduire

Mots clés en anglais

Cell segmentation

clustered cells

grey-weighted distance transform

light intensity

negative phase contrast images

shape

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Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information

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