title: Automated classification of mitotic phenotypes of human cells using fluorescent proteins.
abstract: High-throughput screens of the gene function provide rapidly increasing amounts of data. In particular, the analysis of image data acquired in genome-wide cell phenotype screens constitutes a substantial bottleneck in the evaluation process and motivates the development of automated image analysis tools for large-scale experiments. In this chapter, we present a computational scheme to process multicell time-lapse images as they are produced in high-throughput screens. We describe an approach to automatically segment and classify cell nuclei into different mitotic phenotypes. This enables automated identification of cell cultures that show an abnormal mitotic behavior. Our scheme proves high classification accuracy, suggesting a promising future for automating the evaluation of high-throughput experiments.
authors.affiliation: Department of Bioinformatics and Functional Genomics, German Cancer Research Center (DKFZ), University of Heidelberg, IPMB Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany.
authors.forename: N
authors.initials: N
authors.lastname: Harder
authors.forename: R
authors.initials: R
authors.lastname: Eils
authors.forename: K
authors.initials: K
authors.lastname: Rohr
mesh_terms: D000465:Algorithms; D017209:Apoptosis; D002467:Cell Nucleus / Q000502:physiology* / Q000648:ultrastructure; D006367:HeLa Cells; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D008164:Luminescent Proteins; D008856:Microscopy, Fluorescence / Q000379:methods*; D008938:Mitosis; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype
chemical_list: D008164:Luminescent Proteins