Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Software

Enhanced CellClassifier: a multi-class classification tool for microscopy images

Benjamin Misselwitz1*, Gerhard Strittmatter1, Balamurugan Periaswamy1, Markus C Schlumberger1, Samuel Rout1, Peter Horvath2, Karol Kozak2 and Wolf-Dietrich Hardt1

Author Affiliations

1 Institute of Microbiology, ETH Zurich, Wolfgang Pauli-Str. 10, 8093 Zürich, Switzerland

2 Light Microscopy Centre, Institute of Biochemistry, ETH Zürich, Schafmattstr. 18, 8093 Zürich, Switzerland

For all author emails, please log on.

BMC Bioinformatics 2010, 11:30  doi:10.1186/1471-2105-11-30

Published: 14 January 2010

Abstract

Background

Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories.

Results

We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables.

Conclusion

Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening.