![]() In our scheme, nuclei are first extracted from the DNA channel and labeled, which are then used as the initialization for segmentation of cells in other channels. The Rac channel contains some auxiliary information. Only the nucleus of the cell is visible in the DNA channel, while the cytoplasm of the cell is only available in an Actin channel. The three channels, Actin, Rac, and DNA, are captured, as shown in Fig. Each staining is captured by one image channel. In our biological study, cells are fixed and stained with different dyes to investigate the different components of cells. (c) Actin-acceleration-at-edge (A-type).Ī novel cellular image segmentation scheme for RNAi fluorescent genome-wide screening is presented to address these challenges. Patches of RNAi cell image in the Actin channel with specific phenotypes. Thirdly, cells are tightly clustered and will not likely show a strong border where they touch, which makes it difficult to separate the cells. Thus, areas inside each cell can no longer be assumed as homogeneous regions, which cause many intensity-based segmentation methods fail. Secondly, significant intensity variations exist in each cell. These shapes are not always convex, and violate the assumption of many algorithms. They are used for the gene clustering and scoring, and are of particular interest to our biologists. Samples of the three major phenotypes, S-spikey, R-ruffling, and A-actin acceleration at edge, to be identified in this study, are shown in Fig. Firstly, cells with specific phenotypes need to be accurately segmented for further analysis. Three main challenges, related to this task are as follows. The key problem here is to automatically segment cells from cell-based assays in a cost-effective manner, since fast screening can generate hundreds of thousands of images in each study with rather poor image quality. Clearly, there is a growing need for automated image analysis as high-throughput technologies are being extended to visual screens. Each screen generates more than 400 000 images, or even millions if replicas are included. After incubation with the dsRNA, cells are fixed, stained, and imaged by automated microscopy. Drosophila cells are plated, and take up dsRNA from culture media. For instance, in a typical study conducted by our biologists, approximately 21 000 dsRNAs specific to the predicted Drosophila genes are robotically arrayed in 384-well plates. However, without the aid of computerized image analysis, it is almost intractable to quantitatively characterize morphological phenotypes and identify genes in high-throughput screening. In a small-scale study using manual analysis of genome-wide screening, biologists were able to observe a wide range of phenotypes with affected cytoskeletal organization and cell shape. The development of Drosophila RNAi technology to systematically disrupt gene expression enables screening of the entire genome for specific cellular functions. By using the RNA interference (RNAi) process, the function of a gene can be determined by inspecting changes in a biological process caused by the addition of gene-specific double-stranded RNA (dsRNA). High-throughput screening using automated fluorescent microscopy is becoming an essential tool to assist biologists in understanding complex cellular processes and genetic functions. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. The large number of images produced in each study make manual analysis intractable hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes.
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