Training course on Visualisation, AI-Assisted Analysis and Quantification of Tomographic Datasets
| Dates: | 28 September 2026 - 29 September 2026 |
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| Times: | 09:00 - 17:00 |
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| What is it: | Course |
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| Organiser: | The University of Manchester at Harwell |
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| How much: | Academics, students, postdocs, government: £400.00 + VAT. Industry: £800.00 + VAT |
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| Who is it for: | University staff, External researchers, Adults, Current University students |
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General course information
Lab/synchrotron X-ray tomography has emerged as one of the most important techniques for research in a wide range of applications including healthcare, energy, food and geology. Advances in the quality of X-ray beams, optics, high speed data acquisition and in-situ environments have made significant improvements to non-destructive imaging of a specimen structure in 3D and over a wide range of length scales (micron- and nanoscales). Although the direct benefit of X-ray tomography is the 3D visualisation of the internal structure of specimens, which is very valuable to understand the structure/function relationships, the technique has however a lot more to offer. The information is hidden in the data, and robust methods and workflows — increasingly powered by AI/deep learning — are needed to extract it in a relevant and accurate way, and make it readily available for decision making.
Organisation
The training is organised in three parts: theory of image processing and deep learning (0.5 day), and computer-based practical work using the Thermo Scientific™ Amira-Avizo Software 3D (1.5 day), including its AI-based tools for denoising and segmentation. At the end of the second day, the users are invited to practise on their own dataset with the help of the trainers. The attendees are invited to bring their own laptop (an Avizo license will be provided for the training).
Who should attend?
This course is aimed at both beginners with no prior experience in 3D imaging and image processing, and intermediate-level researchers who want to know more about it and explore different ways of analysing 3D datasets, including the latest AI-assisted workflows.
Learning outcomes
• Basics of image processing and mathematical morphology
• Introduction to the theory of Deep Learning: what neural networks are and how they learn, convolutional neural networks (CNNs) and why they suit image data, how a network is trained (data, labels, loss, epochs), and the practical implications for tomographic data (e.g. data/label quality, generalisation, and when AI methods are a better fit than classical ones)
• Import/manipulate tomographic dataset
• Surface/volume renderings
• Filtering
• AI-based denoising of noisy/low-dose tomographic data
• Basic/advanced segmentation
• AI-based segmentation, including 2D and 2.5D deep learning approaches
• Quantification
• Animation/movies
How to register:
Please contact Sarah Batts if you wish to sign up for this course (sarah.batts@manchester.ac.uk). Places are limited to 10 attendees per course. Courses generally fill up very quickly and places are allocated on a first come first serve basis. If there is low attendance, a course may be cancelled with a month’s notice.
Price: Academics, students, postdocs, government: £400.00 + VAT. Industry: £800.00 + VAT
Travel and Contact Information
Find event
Rutherford Appleton Laboratory Visitors Centre - Hamilton Room
Fermi Avenue
Harwell Campus
Didcot
Oxfordshire
