Projects & Software

Current research projects

Adipose and muscular tissue segmentation

For the department of Health Evidence we are developing an application for the segmentation and quantification of adipose and muscular tissue in non-contrast abdominal CT.

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AI for health

A collaboration between Radboud University and Radboudumc aimed at developing innovations based on artificial intelligence that solve clinical problems.

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AQUILA

The goal of AQUILA is to investigate the prognostic value of Tumor Infiltrating Lymphocytes (TILs) in breast and colon cancer.

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Brain MRI classification

In the context of the “Nationale Wetenschapsagenda” (NWA) roadmap for Value Creation through Responsible Access to and use of Big Data (VWdata) we are collecting data and setting up a challenge for the identification of healthy or abnormal MRI scans of the brain.

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ExaMode

The aim of ExaMode is to collect training data with limited human interaction for the processing of exascale volumes of healthcare data.

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ICH and PHE segmentation

For the CONTRAST consortium and Dutch ICH Surgery Trial (DIST) we are developing an application for the segmentation and quantification of intracerebral hemorrhage and peri-hematomal edema in non-contrast CT.

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Knee segmentation

For the Orthopedics department we are developing an application for the segmentation of the tibia, patella and fibula in non-contrast CT images of the legs.

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Artificial intelligence for lung cancer screening

We aim to improve the efficiency of lung cancer screening by using artificial intelligence.

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Muscle ultrasound classification

For the Neurophysiology department we have developed an algorithm for the identification of abnormal muscle tissue in ultrasound images of the tibialis anterior.

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Software

CIRRUS

CIRRUS is the workstation platform for DIAG

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CIRRUS Lung Screening

This workstation is a highly optimized reading platform that allows fast and standardized reading of lung screening CT scans.

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Grand-challenge.org

The home of challenges in biomedical imaging.

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SOL

RTC Deep Learning's cluster for training and applying automated image analysis tools.

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Finished projects

AMI

Automation in Medical Imaging - a joint project between DIAG and Fraunhofer MEVIS aimed at the development of a generic platform for automatic medical image analysis.

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