Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging

Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluorescence microscopy images. In this protocol, we de...

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Bibliographic Details
Published inMethods in molecular biology (Clifton, N.J.) Vol. 2476; p. 17
Main Authors Ulicna, Kristina, Ho, Laure T L, Soelistyo, Christopher J, Day, Nathan J, Lowe, Alan R
Format Journal Article
LanguageEnglish
Published United States 2022
Subjects
Image analysis
Live-cell imaging
Cell cycle
Machine learning
Computational biology
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Summary:Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluorescence microscopy images. In this protocol, we develop user-friendly tools to perform this task. We provide an open-source annotation tool, and a cloud-based computational framework to train and utilize a convolutional neural network to automatically classify chromatin morphology. Using cloud compute enables users without significant resources or computational experience to use a machine learning approach to analyze their own microscopy data.
ISSN:1940-6029
DOI:10.1007/978-1-0716-2221-6_3