Farhad Mohammad Kazemi
M.Sc. Candidate
Co-Supervisors: Drs. Wolfgang Banzhaf and Minglun Gong

Automatic Image Analysis for High Content Screening

Department of Computer Science
Friday, May 26, 2017, 11:00a.m., Room EN 2022

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Abstract

High content screening technologies deal with screening thousands of cells which can provide a number of parameters for each cell such as nuclear size, nuclear morphology, DNA replication, etc. The success of High Content Screening (HCS) systems is dependent on automated image analysis. This research focuses on the challenge of using information content that is as high as possible, by considering per-cell information and all the available features, to build a system for high content analysis. In this work, we developed a model to classify MOAs based on phenotypic screens of a number of cells in each image. We demonstrated that our model can predict the mechanisms of action (MOA) for a compendium of drugs that alter cells, without any segmentation and feature extraction steps.