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Details of Grant 

EPSRC Reference: EP/Y008715/1
Title: Bio-image processing at exascale
Principal Investigator: Muresan, Dr L
Other Investigators:
Edsall, Mr CJ Robinson, Dr H
Researcher Co-Investigators:
Project Partners:
Medical Research Council (MRC) University of Bath Yale University
Department: Physiology Development and Neuroscience
Organisation: University of Cambridge
Scheme: Standard Research
Starts: 01 June 2023 Ends: 30 November 2024 Value (£): 447,849
EPSRC Research Topic Classifications:
Image & Vision Computing Software Engineering
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
20 Apr 2023 SPF Emerging Requirements Interview panel Announced
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.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Organisation Website: http://www.cam.ac.uk