Deep learning classification of lipid droplets in quantitative phase images

Sheneman, Luke; Stephanopoulos, Gregory; Vasdekis, Andreas E.; Qiu, ed., Yuchen

doi:10.1371/journal.pone.0249196

Title: Deep learning classification of lipid droplets in quantitative phase images

Journal Article · Mon Apr 05 00:00:00 EDT 2021 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0249196· OSTI ID:1773959

; Stephanopoulos, Gregory; Vasdekis, Andreas E.; Qiu, ed., Yuchen

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

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Research Organization:: Univ. of Idaho, Moscow, ID (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0019249

OSTI ID:: 1773959

Alternate ID(s):: OSTI ID: 1773779

Journal Information:: PLoS ONE, Journal Name: PLoS ONE Vol. 16 Journal Issue: 4; ISSN 1932-6203

Publisher:: Public Library of Science (PLoS)Copyright Statement

Country of Publication:: United States

Language:: English

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