| EPSRC Reference: |
EP/Y008715/1 |
| Title: |
Bio-image processing at exascale |
| Principal Investigator: |
Muresan, Dr L |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Physiology Development and Neuroscience |
| Organisation: |
University of Cambridge |
| Scheme: |
Standard Research |
| Starts: |
01 June 2023 |
Ends: |
25 March 2025 |
Value (£): |
447,849
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| EPSRC Research Topic Classifications: |
| Image & Vision Computing |
Software Engineering |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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20 Apr 2023
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SPF Emerging Requirements Interview panel
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Announced
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Summary on Grant Application Form |
Quantitative biology relies heavily on expertise from many fields: biology, computer science, physics, mathematics, engineering, statistics, chemistry. The amount of detailed, specific information related to new techniques might be daunting at an individual level. Traditionally, research groups acquired microscopy data in their lab, stored it on workstations or NAS and a dedicated PhD student or post-doctoral researcher analysed it locally. However, the sheer amount of data, the complexity of the techniques, and the specialised (software/hardware) skills make this workflow unsustainable. The landscape is continuously evolving: cutting edge optical systems are maintained in research facilities, data is stored in data centres and processed on high performing computing (HPC) clusters.
The aim of the project is to streamline the exploitation of the Cambridge Data Accelerator testbed by the biological community, unlocking the potential of cutting edge high performance computing for the analysis of large microscopy image dataset, with special focus on lightsheet imaging. Lightsheet microscopy has experienced a boom in the last decade, being designated technique of the year by Nature Methods in 2014. Due to its advantages such as the low phototoxicity and fast imaging of large volumes, the role of the technique in developmental biology as well as fast calcium or membrane potential indicator imaging cannot be overstated. However, the blocking factor for light sheet microscopy to reach its full potential is a computational one: typical datasets consist of a time sequence of multi-tile, multi-angle, multi-colour 3D data stacks totalling terabytes of data (see data description below) that need complex processing.
While the lightsheet technique is at the high end of the data size spectrum, and the required pre-processing steps are quite specific to the technique, the data management and downstream analysis can be easily extended to a broad range of different microscopy techniques (confocal, two-photon, spinning disk etc.)
In order to engage the community the threshold to access specialised computing resources has to be lowered and an efficient response to arising requests put in place. Our strategy focuses on four axis: (1) minimise the effort required to access the new technology (seamless pre-processing on HPC of acquired lightsheet images, with data management transparent to the user), (2) offer ready to use pipelines and models for well-known cutting edge deep learning methods, the community is familiar with, (3) design a mechanism for bespoke solutions based on test cases from collaborators and most importantly, (4) forging a community, sharing knowledge and skills.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cam.ac.uk |