X-ray tomography image processing with deep learning

Target Group

PhD candidates and postdocs interested in X-ray microCT, image processing and deep learning applications.

No pre-requisite on python programming or image processing is required.

Aim and Topic

X-ray micro computed tomography is an imaging technique widely used in science and engineering. A key component in the workflow of X-ray micro computed tomography is the image processing and analysis required to extract valuable information from the images. In this advanced doctoral training course, we aim to provide a detailed overview on X-ray tomography image processing and the application of deep learning techniques through a series of lectures and hands on practical sessions in Dragonfly and Python.

Upon completing the course, the participants will be familiar with deep learning image processing techniques suitable for analysing X-ray micro computed tomography images and understand where deep learning techniques can be applied and where other image processing techniques are better suited. The participants will be able to carry out the image processing procedures from the practical sessions, consisting of training with Dragonfly (commercial software, free for academic use) and open source software Python. The code from the latter will be made available for the participants.


You can find the programme here.

Dates & Venue

06.02.2023 – 08.02.2023, Campus Coupure, PC A1.3

Registration procedure

Please follow this link: Course is fully booked

For more information on the course, contact Liselotte de Ligne (Liselotte.DeLigne@UGent.be) or Jaianth Vijayakumar (Jaianth.Vijayakumar@UGent.be).

Registration fee

Free of charge for Doctoral School members. The no show policy applies. 

Non-UGent participants will be charged a registration fee of €250. Payment details will be forwarded to non-UGent participants after registration.

Number of participants

Max. 40



Teaching methods

  • Ex-cathedra lectures: 7 hours
  • Poster-session: 2 hours
  • Practical exercise: 14 hours

Evaluation methods and criteria (doctoral training programme)

100% attendance