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

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. Lay description Cell image segmentation is a process of separating the regions of individual cells from background in optical microscope images containing cells. It is a crucial step for cell morphology analysis and cell behaviour characterisation. The cell‐based analyses mostly relay on the massive measurement of hundreds or even thousands of cells in a single experiment. As a result, high throughput image screening obtained with time‐lapse microscope imaging is widely applied. Therefore, automated cell segmentation becomes necessary in cell‐based biology research an...
Source: Journal of Microscopy - Category: Laboratory Medicine Authors: Tags: Original Article Source Type: research