Infrastructure Overview

Figure 1. 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 studygovernor and a tasksupervisor. 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.

Anonymization and data transport

Generally we use the Clinical Trial Processor (CTP) for anonymization, secure data transport and allocating data to a project. CTP allows the creation of processing pipelines for DICOM scans, in which scans are received, processed and forwarded to another DICOM receiver.

Medical imaging data storage using XNAT

At the heart of the infrastructure is the storage of the medical images. For this we use XNAT, software for a research medical image archive created by the Washington University in St. Louis. We host our own servers within our institute, but also maintain the Dutch national XNAT server hosted by CTMM-TraiT. We also created and maintain a Python library to communicate with XNAT called xnatpy.
XNAT is used to standardize the image storage, making it easier to automate. XNAT also allows for Poject based data access management.

Compute infrastructure

In medical imaging the extraction of a biomarker from an image is mostly not a single step, but an entire processing pipeline. To formalize these pipelines and help executing them in a consistent manner on compute clusters or the cloud, we created the fastr workflow software. To track the progress and status of complex pipelines our colleagues in the Leiden University Medical Center (LUMC) created the Pipeline Inspection and Monitoring (PIM) platform.

Data and workflow management services

The data and workflow management services are a collection of tools and services to manage the data flow, including manual interaction with the data, in an automated fashion. The key components are the Study Governor for automatically managing the data flow and the Task Supervisor for task based manual interaction with the data.

Manual inspection and annotation tasks

The is ViewR is a tool to interact with the data on XNAT by researchers based on the tasks served by the tasksupervisor. The layout and forms in the ViewR are determined by templates supplied by the tasksupervisor. This way the ViewR adapts itself for the task to be performed.