An automated feedback system with the hybrid model of scoring and classification for solving over-segmentation problems in RNAi high content screening

Background: High content screening (HCS) via automated fluorescence microscopy is a powerful technology for generating cellular images that are rich in phenotypic information. RNA interference is a revolutionary approach for silencing gene expression and has become an important method for studying genes through RNA interference-induced cellular phenotype analysis. The convergence of the two technologies has led to large-scale, image-based studies of cellular phenotypes under systematic perturbations of RNA interference. However, existing high content screening image analysis tools are inadequate to extract content regarding cell morphology from the complex images, thus they limit the potential of genome-wide RNA interference high content screening screening for simple marker readouts. In particular, over-segmentation is one of the persistent problems of cell segmentation; this paper describes a new method to alleviate this problem. Methods: To solve the issue of over-segmentation, we propose a novel feedback system with a hybrid model for automated cell segmentation of images from high content screening. A Hybrid learning model is developed based on three scoring models to capture specific characteristics of over-segmented cells. Dead nuclei are also removed through a statistical model. Results: Experimental validation showed that the proposed method had 93.7% sensitivity and 94.23% specificity. When applied to a set of images of F-actin-stained Drosophila cells, 91.3% of over-segmented cells were detected and only 2.8% were under-segmented. Conclusions: The proposed feedback system significantly reduces over-segmentation of cell bodies caused by over-segmented nuclei, dead nuclei, and dividing cells. This system can be used in the automated analysis system of high content screening images.