Abstract-High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. 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. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. 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. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genomewide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems. Index Terms-Fluorescent microscopy, high throughput, image segmentation, interaction model, level set, multichannel.
In 2008, Yan et al. REF proposed a method for segmenting the nuclei of cells from a genome-wide RNAi screening image.
Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Computer Science Medicine