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Proceedings Paper

Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning
Author(s): Steffen Remmele; Julia Ritzerfeld; Walter Nickel; Jürgen Hesser
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Paper Abstract

RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

Paper Details

Date Published: 14 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623I (14 March 2011); doi: 10.1117/12.878097
Show Author Affiliations
Steffen Remmele, Univ. of Heidelberg (Germany)
Julia Ritzerfeld, Heidelberg Univ. Biochemistry Ctr. (Germany)
Walter Nickel, Heidelberg Univ. Biochemistry Ctr. (Germany)
Jürgen Hesser, Univ. of Heidelberg (Germany)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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