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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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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Coronary artery segmentation
For the department of Cardiology we are developing an application for the segmentation of coronary arteries in cardiac OCT recordings.
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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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Quantifying IHC pathology stains
For the Pathology department we are developing an application that can unmix different IHC stains to aid analysist in quantifying pathology scans.
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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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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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SOL
RTC Deep Learning's cluster for training and applying automated image analysis tools.
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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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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