Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information
Résumé
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.
Origine : Fichiers produits par l'(les) auteur(s)
Commentaire : En vertu de la Loi pour une République numérique, l'embargo imposé par l'éditeur sur le post-print est réduit à 6 mois à compter de sa publication en ligne
Commentaire : En vertu de la Loi pour une République numérique, l'embargo imposé par l'éditeur sur le post-print est réduit à 6 mois à compter de sa publication en ligne
Loading...