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Data-driven phenotypic machine learning analysis for 3D microscopy
Unbiased quantitative phenotypic analysis of microscopy images of cells or organoids grown in 3D in large enough numbers to reach statistical clarity remains a fundamental challenge. Mergenthaler, Hariharan et al. report that using data driven voxel-based features and machine learning it is possible to analyze complex 3D image data without compressing them to 2D, identifying individual cells or using computationally intensive deep learning techniques. They provide the means for facile discovery and interpretation of meaningful patterns in a high dimensional feature space without complex image processing and prior knowledge or assumptions about the feature space.
Image Credit: Philipp Mergenthaler, Charité - Universitätsmedizin Berlin, Santosh Hariharan and David Andrews, Sunnybrook Research Institute, Toronto.
Citation: (2021) PLoS Computational Biology Issue Image | Vol. 17(2) March 2021. PLoS Comput Biol 17(2): ev17.i02. https://doi.org/10.1371/image.pcbi.v17.i02
Published: March 2, 2021
Copyright: © 2021 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Unbiased quantitative phenotypic analysis of microscopy images of cells or organoids grown in 3D in large enough numbers to reach statistical clarity remains a fundamental challenge. Mergenthaler, Hariharan et al. report that using data driven voxel-based features and machine learning it is possible to analyze complex 3D image data without compressing them to 2D, identifying individual cells or using computationally intensive deep learning techniques. They provide the means for facile discovery and interpretation of meaningful patterns in a high dimensional feature space without complex image processing and prior knowledge or assumptions about the feature space.
Image Credit: Philipp Mergenthaler, Charité - Universitätsmedizin Berlin, Santosh Hariharan and David Andrews, Sunnybrook Research Institute, Toronto.