Medical imaging is the field of creating images to display the anatomy and diseases in the human body and of developing computer algorithms that extract information from those images to aid clinicians in diagnosing disease, making therapy plans and performing treatment.

The Medical Image Analysis group at TU/e focusses on developing AI methods for both the acquisition of medical images and the automatic analysis of those images. Research on image acquisition aims to improve medical images, for instance, by reducing artefacts and noise, by reducing acquisition times, by reducing the dose required for acquisition or by thinking of new ways to make anatomical structures or disease visible in images. Examples are MR navigator techniques to correct for breathing motion during scanning, smart sampling trajectories in k-space for fast acquisition and deep learning to improve images reconstructed from low-dose CT. Research on image analysis aims to automatically detect disease in images, to classify what is found into categories, to measure structures or to track moving objects. Examples are methods that can support large screening studies for early detection of diseases (‘bevolkingsonderzoek’), methods that classify suspicious lesions in the breast into type of tumour to help decide on the optimal treatment, and methods that measure the size of the brain as a biomarker for Alzheimer’s disease.

In both acquisition and analysis, state-of-the-art AI methods can have substantial impact on the accuracy, safety and costs of medical care. Most of our research is in close collaboration with industry and/or hospitals

Variants – BME/ME/MIx

Medical Image Analysis offers both the Biomedical Engineering (BME) and Medical Engineering (ME) programme. In addition, there is a joint special master’s track with the Center for Image Sciences at UMC Utrecht, called Medical Imaging (MIx), with students enrolled from both Utrecht and Eindhoven. You can choose to either do a regular BME or ME programme in Eindhoven or to do those in the joint variant with Utrecht, i.e. BME-MIx or ME-MIx.

Medical Imaging (MIx) – with UMC Utrecht

The joint master’s track allows you to take courses both at TU/e and at UMCU. The Center for Image Sciences in Utrecht is a large research institute in medical imaging, working in close collaboration with clinical departments. It hosts numerous regular scanners (e.g. CT, MRI, SPECT) as well as novel research scanners (dual energy CT, MR-linac, MR-hifu).

Course programme

The study programme adheres to the standard BME and ME programmes of the Department, with ECs for specialisation courses, free space, an externship and a master’s project. The difference between BME/ME at TU/e or BME/ME in the joint MIx master is the list from which you choose your specialisation courses. You can find the lists in the Education Guide.

You can include courses from other departments in your programme, but do bear in mind that some courses have substantial overlap with our own and are therefore excluded. You can find some common cases below, in the section on Courses.

More questions?

You can find further information on the FAQ page.


The medical image analysis group provides the following courses:

Name of the course
Course codeTeachersQuartile
Beeldvorming en -verwerking8DB00Josien Pluim
Alexander Raaijmakers
Richard Lopata
Medische Beeldanalyse8DC00Cian Scannell1
Voortgezette Beeldvormende Technieken8VC00Richard Lopata
Alexander Raaijmakers
Project AI for Medical Imaging8P361Mitko Veta3
Team Challenge in Medical Imaging8DM10Mitko Veta
Koen Vincken
Josien Pluim
Machine Learning in Medical Imaging and Biology8DM50Mitko Veta
Federica Eduati
Electromagnetic fields in MRI5LPE0Alexander RaaijmakersGS2
Capita Selecta in Medical Image Analysis8DM20Josien Pluim
Marcel Breeuwer
Cian Scannell

Overlapping courses

Name of the courseOverlaps with
8UU22 AI for Medical Imaging8DC00, 8P361 and 8DM50
5XSM0 MRI for the brain8VC00
5XSL0 Fundamentals of machine learning8DM50
5LSM0 Convolutional neural networks for computer vision8DM50 and 8DM20
The courses in the left column may not be approved in your study programme because of overlap with other courses.