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.
The Gleason score is the most powerful prognostic marker for prostate cancer patients. Unfortunately, when pathologists assign this score from visually analyzing tissue slides, there is a large inter- and intra-observer variability. Deep learning may alleviate this problem. Therefore Wouter Bulten and his colleagues from DIAG developed an automated Gleason scoring system. The work appeared in The Lancet Oncology.
The figure above shows the development of the deep learning system. Data was labeled semi-automatically (top row), removing the need for manual annotations by pathologists. The final system assigns a Gleason growth patterns on a cell-level and achieved a high agreement with the reference standard (quadratic kappa 0.918). In a separate observer experiment, the deep learning system outperformed 10 out of 15 pathologists in agreement with the reference standard. The system was validated on an external test set where it achieved an AUC of 0.977 for distinguishing between benign and malignant biopsies.
Click here to try Wouter's algorithm on your own data and learn more about the project on automated Gleason grading.
More Research Highlights.