Medical Imaging Interaction Toolkit (MITK)

MITK is a very modular and versatile open-source software development platform for applications in medical image processing. It's being developed at the German
Cancer Research Center since 2002 with contributions and users from an
international community in research and industry.

-> MITK Team

-> MITK Homepage


MITK Workbench


The MITK Workbench is a free medical image viewer, which supports all kinds of DICOM modalities like CT, MRT, US and RT as well as a number of research file formats. In addition to 2D, 3D and 3D+t visualization capabilities, there are numerous plugins for segmentation, registration and other processing steps available.


-> MITK Team

-> MITK Homepage

Research data management and automated processing

While the focus of the MITK Workbench is interactive processing, today's research questions often require the standardized and automated processing of large datasets. We integrate MITK with the Extensible Neuroimaging Archive Toolkit (XNAT), NoSQL databases and private cloud solutions like OpenStack to provide and evaluate new exploration and processing capabilities to medical imaging researchers.


 -> Caspar Goch

Internal support and infrastructure

Within the research program "Imaging and Radiooncology", the MICO team develops and provides solutions for scientific software development and testing infrastructure in various departments. Building on the experience of MITK development, we provide source code control and issue trackers as well as custom support for e.g. the integration of applications and data types in MITK-based workflows.


 -> Caspar Goch

 -> Stefan Dinkelacker

Joint Imaging Platform

Within the German Cancer Consortium (DKTK) the Joint Funding Project “Joint Imaging Platform” will establish a distributed IT infrastructure for image analysis and machine learning in the member institutions. It will facilitate pooling of analysis methods that can be applied in an automated and standardized manner to patient data in the different centers, allowing for unprecedented cohort sizes. The biggest research challenge is the combination, aggregation and distribution of training data, processes and models for non-shareable sensitive data as well as the validation of quantitative imaging biomarkers across a multi-institutional consortium. On the implementation side we investigate distributed learning methods as well as latest private cloud technologies for a robust deployment of data management and processing.


  -> Jonas Scherer

 -> Jasmin Metzger


Intraoperative assistance system for mobile C-Arm devices

Intraoperative imaging can help to improve the quality of reduction results in trauma surgery. However, the mobility of the device comes with lack of information about its orientation to the patient. Screws, plates and pathologies further increase the challenge of image understanding. We aim to assist the surgeon in upper ankle surgery by incorporating prior knowledge and information of the contralateral site using 2D/3D registration.


 -> Sarina Thomas


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