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Automated Microscopy Image Segmentation and Analysis with Machine Learning

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Fluorescent Microscopy

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2440))

Abstract

The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability between annotators. Machine/Deep Learning (ML/DL) now provides tools to automatically extract the set of rules to obtain quantitative information from the images (e.g. segmentation, enumeration, classification, etc.). Many parameters must be considered in the development of proper ML/DL pipelines. We herein present the important vocabulary, the necessary steps to create a thorough image segmentation pipeline, and also discuss technical aspects that should be considered in the development of automated image analysis pipelines through ML/DL.

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Acknowledgements

Annette Schwerdtfeger for careful proofreading of the manuscript. Funding was provided by grants from the Natural Sciences and Engineering Research Council of Canada and the Neuronex Initiative (National Science Foundation, Fond de recherche du Québec – Santé). F.L.C. is a Canada Research Chair Tier II, A.B. and C.B. are both supported by a PhD scholarship from the Fonds de recherche du QuÕbec - Nature et technologies (FRQNT), an excellence scholarship from the FRQNT strategic cluster UNIQUE, and C.B. by a Leadership and Scientific Engagement Award from Université Laval.

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Authors and Affiliations

  1. Université Laval, Québec, QC, Canada

    Anthony Bilodeau & Catherine Bouchard

  2. CERVO Brain research center, Québec, QC, Canada

    Anthony Bilodeau & Catherine Bouchard

  3. CERVO Brain research center, Québec, QC, Canada

    Flavie Lavoie-Cardinal

  4. Département de psychiatrie et de neurosciences, Université Laval, Québec, QC, Canada

    Flavie Lavoie-Cardinal

Authors
  1. Anthony Bilodeau
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  2. Catherine Bouchard
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  3. Flavie Lavoie-Cardinal
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Corresponding author

Correspondence to Flavie Lavoie-Cardinal .

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Editors and Affiliations

  1. Department of Microbiology and Immunology, Center for Human Immunology, The University of Western Ontario, London, ON, Canada

    Bryan Heit

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Bilodeau, A., Bouchard, C., Lavoie-Cardinal, F. (2022). Automated Microscopy Image Segmentation and Analysis with Machine Learning. In: Heit, B. (eds) Fluorescent Microscopy. Methods in Molecular Biology, vol 2440. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2051-9_20

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  • DOI: https://doi.org/10.1007/978-1-0716-2051-9_20

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2050-2

  • Online ISBN: 978-1-0716-2051-9

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