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Title: ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

Abstract

The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli ( E. Coli ), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usabilitymore » of the software are also discussed.« less

Authors:
; ; ; ORCiD logo; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1894531
Resource Type:
Published Article
Journal Name:
Journal of Synchrotron Radiation (Online)
Additional Journal Information:
Journal Name: Journal of Synchrotron Radiation (Online) Journal Volume: 29 Journal Issue: 6; Journal ID: ISSN 1600-5775
Publisher:
International Union of Crystallography (IUCr)
Country of Publication:
Denmark
Language:
English

Citation Formats

Chowdhury, M. A. Z., Ok, K., Luo, Y., Liu, Z., Chen, S., O'Halloran, T. V., Kettimuthu, R., and Tekawade, A. ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy. Denmark: N. p., 2022. Web. doi:10.1107/S1600577522008876.
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Chowdhury, M. A. Z., Ok, K., Luo, Y., Liu, Z., Chen, S., O'Halloran, T. V., Kettimuthu, R., & Tekawade, A. ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy. Denmark. https://doi.org/10.1107/S1600577522008876
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Chowdhury, M. A. Z., Ok, K., Luo, Y., Liu, Z., Chen, S., O'Halloran, T. V., Kettimuthu, R., and Tekawade, A. Tue . "ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy". Denmark. https://doi.org/10.1107/S1600577522008876.
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@article{osti_1894531,
title = {ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy},
author = {Chowdhury, M. A. Z. and Ok, K. and Luo, Y. and Liu, Z. and Chen, S. and O'Halloran, T. V. and Kettimuthu, R. and Tekawade, A.},
abstractNote = {The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli ( E. Coli ), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed.},
doi = {10.1107/S1600577522008876},
journal = {Journal of Synchrotron Radiation (Online)},
number = 6,
volume = 29,
place = {Denmark},
year = {Tue Oct 25 00:00:00 EDT 2022},
month = {Tue Oct 25 00:00:00 EDT 2022}
}
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