Principles of bioimage informatics: focus on machine learning of cell patterns

Authors:
Luis Pedro Coelho;Estelle Glory-Afshar;Joshua Kangas;Shannon Quinn;Aabid Shariff;Robert F. Murphy
Affiliations:
Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology;Center for Bioimage Informatics, Carnegie Mellon University;Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology;Lane Center for Computational Biology, Carnegie Mellon University;Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology;Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology
Venue:
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Year:
2009

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Abstract

The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Traditionally, analysis of such images has been done manually. Manual annotation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making manual analysis infeasible. This field borrows from the pattern recognition and computer vision literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular location determination as a running example, but the techniques discussed are often applicable to other problems.