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PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
 M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260909T141932Z
DTSTART:20260928T080000Z
DTEND:20260929T160000Z
SUMMARY:Training course on Visualisation\, AI-Assisted Analysis and Quant
 ification of Tomographic Datasets
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}j1zg-mnr7rj
 a6-wn9ot3
DESCRIPTION:General course information\n\nLab/synchrotron X-ray tomograph
 y has emerged as one of the most important techniques for research in a 
 wide range of applications including healthcare\, energy\, food and geol
 ogy. Advances in the quality of X-ray beams\, optics\, high speed data a
 cquisition and in-situ environments have made significant improvements t
 o non-destructive imaging of a specimen structure in 3D and over a wide 
 range of length scales (micron- and nanoscales). Although the direct ben
 efit of X-ray tomography is the 3D visualisation of the internal structu
 re of specimens\, which is very valuable to understand the structure/fun
 ction 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.\n\nOrganisation\n\nThe training is organised in three parts: the
 ory 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 traini
 ng).\n\nWho should attend?\nThis course is aimed at both beginners with 
 no prior experience in 3D imaging and image processing\, and intermediat
 e-level researchers who want to know more about it and explore different
  ways of analysing 3D datasets\, including the latest AI-assisted workfl
 ows.\n\nLearning outcomes\n\n•	Basics of image processing and mathematic
 al morphology\n•	Introduction to the theory of Deep Learning: what neura
 l 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 bet
 ter fit than classical ones)\n•	Import/manipulate tomographic dataset\n•
 	Surface/volume renderings\n•	Filtering\n•	AI-based denoising of noisy/l
 ow-dose tomographic data\n•	Basic/advanced segmentation\n•	AI-based segm
 entation\, including 2D and 2.5D deep learning approaches\n•	Quantificat
 ion\n•	Animation/movies\n\nHow to register: \nPlease 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.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Rutherford Appleton Laboratory Visitors Centre - Hamilton Room\,
  Fermi Avenue\, Harwell Campus\, Didcot\, Oxfordshire\, OX11 0QX
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