Machine Learning for Analysis of Microscopy Images: A Practical Guide and Latest Trends

Abstract

The explosive growth of Machine Learning provided scientists with insights into the data in the ways unattainable using established research techniques. It allowed the detection of biological features that were previously unrecognized and overlooked. Yet, since Machine Learning methodology originates from informatics, many cell biology laboratories experience difficulties with implementing it. In preparing this article, we targeted the rapidly expanding audience of cell and molecular biologists who perform analysis of microscopy images and seek to add Machine Learning models to their research workflow. We review the advantages of using Machine Learning in microscopy projects, describe the Machine Learning pipeline, and share practical guidelines for building the models. The latest developments in the rapidly expanding field are also given. The technical survey is concluded with an overview of the tools required for model creation and advice on their use.