RTC Deep Learning

We use machine learning, specifically deep learning, to analyze medical images and other medical data. We have set up a high-performance GPU cluster on which deep learning systems can be trained and deployed. We provide advise and develop algorithms and web-based image analysis and data analytics software.

Services and expertise


RTC Deep Learning can provide expert guidance and services for big data analysis and deep learning, specifically in the field of image analysis, but also on predictive analytics in general.

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What is deep learning?

Deep learning is an AI technique that is able to learn complex patterns in large data sets.

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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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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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Infrastructure & Software


CIRRUS is the workstation platform for DIAG

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The home of challenges in biomedical imaging.

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

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