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Accelerating single molecule localisation microscopy through parallel processing on a high-performance computing cluster

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Title: Accelerating single molecule localisation microscopy through parallel processing on a high-performance computing cluster
Authors: Munro, I
Garcia, EAC
Yan, M
Guldbrand, S
Kumar, S
Kwakwa, K
Dunsby, C
Neil, M
French, P
Item Type: Journal Article
Abstract: Super‐resolved microscopy techniques have revolutionized the ability to study biological structures below the diffraction limit. Single molecule localization microscopy (SMLM) techniques are widely used because they are relatively straightforward to implement and can be realized at relatively low cost, e.g. compared to laser scanning microscopy techniques. However, while the data analysis can be readily undertaken using open source or other software tools, large SMLM data volumes and the complexity of the algorithms used often lead to long image data processing times that can hinder the iterative optimization of experiments. There is increasing interest in high throughput SMLM, but its further development and application is inhibited by the data processing challenges. We present here a widely applicable approach to accelerating SMLM data processing via a parallelized implementation of ThunderSTORM on a high‐performance computing (HPC) cluster and quantify the speed advantage for a four‐node cluster (with 24 cores and 128 GB RAM per node) compared to a high specification (28 cores, 128 GB RAM, SSD‐enabled) desktop workstation. This data processing speed can be readily scaled by accessing more HPC resources. Our approach is not specific to ThunderSTORM and can be adapted for a wide range of SMLM software.
Issue Date: 1-Feb-2019
Date of Acceptance: 18-Nov-2018
URI: http://hdl.handle.net/10044/1/66457
DOI: https://dx.doi.org/10.1111/jmi.12772
ISSN: 1365-2818
Publisher: Wiley
Start Page: 148
End Page: 160
Journal / Book Title: Journal of Microscopy
Volume: 273
Issue: 2
Copyright Statement: © 2018 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Medical Research Council
Funder's Grant Number: MR/K015834/1
Keywords: Science & Technology
Automated image analysis
high-performance computing
super-resolved microscopy
0204 Condensed Matter Physics
0912 Materials Engineering
0601 Biochemistry And Cell Biology
Publication Status: Published
Online Publication Date: 2018-12-03
Appears in Collections:Physics
Faculty of Natural Sciences