Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

Steffen Remmele; Julia Ritzerfeld; Walter Nickel; Jürgen Hesser

doi:10.1117/12.878097

14 March 2011 Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

Steffen Remmele, Julia Ritzerfeld, Walter Nickel, Jürgen Hesser

Author Affiliations +

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79623I (2011) https://doi.org/10.1117/12.878097
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Steffen Remmele, Julia Ritzerfeld, Walter Nickel, and Jürgen Hesser "Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623I (14 March 2011); https://doi.org/10.1117/12.878097

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Image quality

Green fluorescent protein

Image analysis

Machine learning

Biological research

Contamination

Show All Keywords

Keywords/Phrases

Search In:

Publication Years