Overview of the image analysis infrastructure. On the left side are the centers where the data is acquired and anonymized. In the center is the XNAT archive, the StudyManager and a TaskManager. On the top are the study database and the viewing/annotation applications for manual annotations. On the bottom is fastr and a compute cluster/cloud for automated analysis. Finally all derived data is collected and exported in a format the study desires. The different components interface with each other through REST APIs.
Large scale medical studies pose technical and administrative challenges. We created an infrastructure to conduct reproducible medical imaging research.
Our infrastructure was originally devloped for the Rotterdam Scan Study, where we tried to implement a continous flow from acquisition to processing, to analyses, to quantitative imaging biomakers.
Our infrastructure can greatly benefit personalized medicine by making pipelines for imaging biomarker extraction available to researchers and clinicians. Additionally, we create a reference database for different imaging biomarkers, which can be used to compare an individual against the general population. This will enable im- proved re-use of imaging data for diagnostics and prognostics.