Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC

James E. McClure; Junqi Yin; Ryan T. Armstrong; Ketan C. Maheshwari; Sean Wilkinson; Lucas Vlcek; Ying Da Wang; Mark A. Berrill; Mark Rivers

doi:10.1007/978-3-030-63393-6_15

Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC

James E. McClure, Junqi Yin, Ryan T. Armstrong, Ketan C. Maheshwari, Sean Wilkinson, Lucas Vlcek, Ying Da Wang, Mark A. Berrill, Mark Rivers

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Synchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.

Original language	English
Title of host publication	Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers
Editors	Jeffrey Nichols, Arthur ‘Barney’ Maccabe, Suzanne Parete-Koon, Becky Verastegui, Oscar Hernandez, Theresa Ahearn
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	226-239
Number of pages	14
ISBN (Print)	9783030633929
DOIs	https://doi.org/10.1007/978-3-030-63393-6_15
State	Published - 2021
Event	17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020 - Virtual, Online Duration: Aug 26 2020 → Aug 28 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1315 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020
City	Virtual, Online
Period	08/26/20 → 08/28/20

Funding

Acknowledgment. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. An award of computer time was provided by the Frontier Center for Accelerated Application Readiness and the Summit Director’s Discretionary Program. This research also used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.

Keywords

Deep learning · Image processing
High-performance computing
Micro-tomography
Scientific workflows

Access to Document

10.1007/978-3-030-63393-6_15

Cite this

McClure, J. E., Yin, J., Armstrong, R. T., Maheshwari, K. C., Wilkinson, S., Vlcek, L., Da Wang, Y., Berrill, M. A., & Rivers, M. (2021). Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC. In J. Nichols, A. B. Maccabe, S. Parete-Koon, B. Verastegui, O. Hernandez, & T. Ahearn (Eds.), Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers (pp. 226-239). (Communications in Computer and Information Science; Vol. 1315 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63393-6_15

McClure, James E. ; Yin, Junqi ; Armstrong, Ryan T. et al. / Toward real-time analysis of synchrotron micro-tomography data : Accelerating experimental workflows with AI and HPC. Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. editor / Jeffrey Nichols ; Arthur ‘Barney’ Maccabe ; Suzanne Parete-Koon ; Becky Verastegui ; Oscar Hernandez ; Theresa Ahearn. Springer Science and Business Media Deutschland GmbH, 2021. pp. 226-239 (Communications in Computer and Information Science).

@inproceedings{5fae7e31c660416c911bebb0661341db,

title = "Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC",

abstract = "Synchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.",

keywords = "Deep learning · Image processing, High-performance computing, Micro-tomography, Scientific workflows",

author = "McClure, {James E.} and Junqi Yin and Armstrong, {Ryan T.} and Maheshwari, {Ketan C.} and Sean Wilkinson and Lucas Vlcek and {Da Wang}, Ying and Berrill, {Mark A.} and Mark Rivers",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020 ; Conference date: 26-08-2020 Through 28-08-2020",

year = "2021",

doi = "10.1007/978-3-030-63393-6_15",

language = "English",

isbn = "9783030633929",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "226--239",

editor = "Jeffrey Nichols and Maccabe, {Arthur {\textquoteleft}Barney{\textquoteright}} and Suzanne Parete-Koon and Becky Verastegui and Oscar Hernandez and Theresa Ahearn",

booktitle = "Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers",

}

McClure, JE, Yin, J, Armstrong, RT, Maheshwari, KC , Wilkinson, S, Vlcek, L, Da Wang, Y, Berrill, MA & Rivers, M 2021, Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC. in J Nichols, AB Maccabe, S Parete-Koon, B Verastegui, O Hernandez & T Ahearn (eds), Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1315 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 226-239, 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Virtual, Online, 08/26/20. https://doi.org/10.1007/978-3-030-63393-6_15

Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC. / McClure, James E.; Yin, Junqi; Armstrong, Ryan T. et al.
Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. ed. / Jeffrey Nichols; Arthur ‘Barney’ Maccabe; Suzanne Parete-Koon; Becky Verastegui; Oscar Hernandez; Theresa Ahearn. Springer Science and Business Media Deutschland GmbH, 2021. p. 226-239 (Communications in Computer and Information Science; Vol. 1315 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Toward real-time analysis of synchrotron micro-tomography data

T2 - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020

AU - McClure, James E.

AU - Yin, Junqi

AU - Armstrong, Ryan T.

AU - Maheshwari, Ketan C.

AU - Wilkinson, Sean

AU - Vlcek, Lucas

AU - Da Wang, Ying

AU - Berrill, Mark A.

AU - Rivers, Mark

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2021

Y1 - 2021

N2 - Synchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.

AB - Synchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.

KW - Deep learning · Image processing

KW - High-performance computing

KW - Micro-tomography

KW - Scientific workflows

UR - http://www.scopus.com/inward/record.url?scp=85107277651&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-63393-6_15

DO - 10.1007/978-3-030-63393-6_15

M3 - Conference contribution

AN - SCOPUS:85107277651

SN - 9783030633929

T3 - Communications in Computer and Information Science

SP - 226

EP - 239

BT - Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers

A2 - Nichols, Jeffrey

A2 - Maccabe, Arthur ‘Barney’

A2 - Parete-Koon, Suzanne

A2 - Verastegui, Becky

A2 - Hernandez, Oscar

A2 - Ahearn, Theresa

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 26 August 2020 through 28 August 2020

ER -

McClure JE, Yin J, Armstrong RT, Maheshwari KC , Wilkinson S, Vlcek L et al. Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC. In Nichols J, Maccabe AB, Parete-Koon S, Verastegui B, Hernandez O, Ahearn T, editors, Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI - 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 226-239. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-63393-6_15

Toward real-time analysis of synchrotron micro-tomography data: Accelerating experimental workflows with AI and HPC

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this