Imperial College London

ProfessorPaulFrench

Faculty of Natural SciencesDepartment of Physics

Professor of Physics and Vice Dean (Research) - FoNS
 
 
 
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Contact

 

+44 (0)20 7594 7706paul.french Website

 
 
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Assistant

 

Ms Judith Baylis +44 (0)20 7594 7713

 
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Location

 

609Blackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Munro:2019:10.1111/jmi.12772,
author = {Munro, I and Garcia, EAC and Yan, M and Guldbrand, S and Kumar, S and Kwakwa, K and Dunsby, C and Neil, M and French, P},
doi = {10.1111/jmi.12772},
journal = {Journal of Microscopy},
pages = {148--160},
title = {Accelerating single molecule localisation microscopy through parallel processing on a high-performance computing cluster},
url = {http://dx.doi.org/10.1111/jmi.12772},
volume = {273},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Superresolved 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 highperformance computing (HPC) cluster and quantify the speed advantage for a fournode cluster (with 24 cores and 128 GB RAM per node) compared to a high specification (28 cores, 128 GB RAM, SSDenabled) 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.
AU - Munro,I
AU - Garcia,EAC
AU - Yan,M
AU - Guldbrand,S
AU - Kumar,S
AU - Kwakwa,K
AU - Dunsby,C
AU - Neil,M
AU - French,P
DO - 10.1111/jmi.12772
EP - 160
PY - 2019///
SN - 1365-2818
SP - 148
TI - Accelerating single molecule localisation microscopy through parallel processing on a high-performance computing cluster
T2 - Journal of Microscopy
UR - http://dx.doi.org/10.1111/jmi.12772
UR - http://hdl.handle.net/10044/1/66457
VL - 273
ER -