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Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

  • Conference paper
Linking Literature, Information, and Knowledge for Biology

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6004))

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.

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Author information

Authors and Affiliations

  1. Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology,  

    Luis Pedro Coelho, Joshua Kangas, Aabid Shariff & Robert F. Murphy

  2. Lane Center for Computational Biology, Carnegie Mellon University,  

    Luis Pedro Coelho, Joshua Kangas, Shannon Quinn, Aabid Shariff & Robert F. Murphy

  3. Center for Bioimage Informatics, Carnegie Mellon University,  

    Luis Pedro Coelho, Estelle Glory-Afshar, Joshua Kangas, Shannon Quinn, Aabid Shariff & Robert F. Murphy

  4. Department of Biological Sciences, Carnegie Mellon University,  

    Shannon Quinn & Robert F. Murphy

  5. Machine Learning Department, Carnegie Mellon University,  

    Robert F. Murphy

  6. Department of Biomedical Engineering, Carnegie Mellon University,  

    Estelle Glory-Afshar & Robert F. Murphy

Authors
  1. Luis Pedro Coelho
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  2. Estelle Glory-Afshar
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  3. Joshua Kangas
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  4. Shannon Quinn
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  6. Robert F. Murphy
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Editor information

Editors and Affiliations

  1. Bioalma, C/Ronda de Poniente, 4, 2-C, 28760, Tres Cantos, Madrid, Spain

    Christian Blaschke

  2. Computational Biology and Machine Learning Lab, School of Computing, Queen’s University, K7L 3N6, Kingston, ON, Canada

    Hagit Shatkay

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Coelho, L.P., Glory-Afshar, E., Kangas, J., Quinn, S., Shariff, A., Murphy, R.F. (2010). Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns. In: Blaschke, C., Shatkay, H. (eds) Linking Literature, Information, and Knowledge for Biology. Lecture Notes in Computer Science(), vol 6004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13131-8_2

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  • DOI: https://doi.org/10.1007/978-3-642-13131-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-642-13131-8

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