Pelvic magnetic resonance imaging (MRI) is used in the diagnosis of prostate, bladder, rectum, cervix, pancreas cancer and various other diseases such as pain due to adhesions. MRI excels in the depiction of the many soft-tissue structures in the pelvis (bladder, rectum, prostate, cervix, intestine, fat, muscle). Pelvic MRI reading requires good expertise and time to properly read all the images in an imaging study and may contain information yet unexplored for diagnostic use. Research in automation and quantification of pelvic MRI tries to reduce the expertise level and reading time and make readily available all information for diagnostic use.
Automation of Prostate MRI is a prominent research project. Prostate MRI has been shown to help reduce the current number of biopsies by 50% and also find 10-20% of the cancers earlier in a stage where curative treatment is very feasible. We have been exploring automated tools to help read prostate MRI. Deep learning allows us to improve conventional image analysis methods and to explore approaches that were infeasible before. We, therefore, have several projects related to the application of Deep Learning to prostate MRI.
As examples, a recent master project resulted in a convolutional neural network (CNN) deep learning system that can analyze T2-weighted prostate MR (see image on top). It was trained to reproduce the PIRADS score of a radiologist. The system was able to successfully identify suspicious regions. Another example project used a 3D U-Net to segment anatomical structures in pelvic MRI (see image on top). This was a pilot project showing feasibility.
We have several project grants under review. Ambitious students may be able to apply for a Ph.D. position.
A large database of ten-thousand annotated pelvic/prostate MR is available from the Computer aided detection of prostate cancer database. You will be able to build upon existing code for the analysis of prostate MR and extensive knowledge in our group on deep-learning.
The work will be executed in the Diagnostic Image Analysis Group (DIAG) of the Radboud University Nijmegen Medical Centre. DIAG is a leading research group in computer-aided detection and diagnosis (CAD). Radboudumc has one of the most internationally renowned groups in prostate MR radiology.