Abstract
Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.
Highlights
Flow cytometry is a reliable, reproducible and quantitative method for studies of the phenotypes that compose a heterogeneous cell population
There is an uncertainty due to artifacts such as cellular debris and clusters of cells in the cell suspension, and it is difficult to go back and have a closer look at signals which deviate from the normal after the analysis is completed, unless the full experiment is run again. Another important source of information about cells is provided by fluorescence staining in combination with fluorescence microscopy, called image cytometry
In this study we have developed a sequence of processing steps that lead to an automatic cytoplasm segmentation of fluorescence microscopy cell images
Summary
Flow cytometry is a reliable, reproducible and quantitative method for studies of the phenotypes that compose a heterogeneous cell population. There is an uncertainty due to artifacts such as cellular debris and clusters of cells in the cell suspension, and it is difficult to go back and have a closer look at signals which deviate from the normal after the analysis is completed, unless the full experiment is run again. Another important source of information about cells is provided by fluorescence staining in combination with fluorescence microscopy, called image cytometry. In this study we have developed a sequence of processing steps that lead to an automatic cytoplasm segmentation of fluorescence microscopy cell images. The obvious step, not within the scope of this study, is to perform the analysis of cell features in the spatial, temporal and spectral domains using the segmentation result
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