Diagnostic Image Analysis Group

The Diagnostic Image Analysis Group is part of the Departments of Radiology, Nuclear Medicine and Anatomy, Pathology, Ophthalmology, and Radiation Oncology of Radboud University Medical Center. We develop computer algorithms to aid clinicians in the interpretation of medical images and improve the diagnostic process.

The group has its roots in computer-aided detection of breast cancer in mammograms, and we have expanded to automated detection and diagnosis in breast MRI, ultrasound and tomosynthesis, chest radiographs and chest CT, prostate MRI, neuro-imaging, retinal imaging, pathology and radiotherapy. The technology we primarily use is deep learning.

It is our goal to have a significant impact on healthcare by bringing our technology to the clinic. We are therefore fully certified to develop, maintain, and distribute software for analysis of medical images in a quality controlled environment (MDD Annex II and ISO 13485) and we closely collaborate with many companies that use our technology in their products.

On this site you find information about the history of the group and our collaborations, an overview of people in DIAG, current projects, publications and theses, contact information, and info for those interested to join our team.


July, 2020


Researchers Jonas Teuwen and Nikita Moriokov of the Diagnostic Image Analysis Group of the Radboud University Medical Center together with colleagues from the Netherlands Cancer Institute and the Amsterdam Medical Center won an international competition where MRI-scans can be accelerated using deep learning algorithms. Their deep learning algorithm won the competition resulting in images of high quality within 2 minutes of scanning.

The competition, which was part of MIDL 2020, was organized by universities in Canada and Brazil who have provided more than 100 brain MRI scans to train the algorithm. The team won both tracks of the challenge, where in the first track the goal was to reconstruct data similar to the training data with 5 and 10 times acceleration, and the second track where the algorithms had to reconstruct out-of-distribution data of a different scanner with different coil configurations. The reconstruction framework is published under an open source license on GitHub, including the winning algorithm.

The methods developed in the challenge have tight connections to our current research lines in MRI-guided radiotherapy where the patient anatomy can be visualized during radiation treatment using the builtin MRI scanner. Fast reconstruction algorithms such as the ones in the challenge open opportunities to track second-by-second patient motion.

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