The  “Open Ground Truth Training Network” (openGTN) research project is about the development of methods for the generation of large quantities of simulated magnetic resonance image (MRI) data, with annotations of relevant anatomies. These data are needed for the training and validation of image analysis algorithms (registration, segmentation, quantification), especially for machine learning approaches such as deep learning using convolutional neural networks (goal 1). In openGTN the focus will be on image segmentation.

The project aims to use these simulated MRI data to develop MRI segmentation methods that are as much as possible independent of the specific MR image contrast (T1-weighted, T2-weighthed, etc.), and insensitive to patient and MRI scanner variations (goal 2).

About openGTN project - Goal and complete approach

The project also aims to make the simulated MRI data and annotations available in public databases, which can be used for optimizing and benchmarking image analysis algorithms (often called “algorithm challenges”, see e.g. grand-challenge.org) (goal 3).

About the research team

The research in openGTN will be performed within a network of 7 R&D institutes spread over Europe. The Medical Image Analysis group (IMAG/e) of the Department of Biomedical Engineering of the Eindhoven University of Technology, The Netherlands, will coordinate the project and will host 3 Early Stage Researchers (ESRs), supported by the highly prestigious and competitive Marie Curie Innovative Training Networks (ITN) fellowship program.

More information about the research team can be found under here.

Research work packages

The openGTN research is organized in 3 work packages (WPs), with 4 specific research objectives (RO).

About openGTN project - Goal and work packages

MRI simulation

The simulation of realistic MR image data of the human brain, spine and heart will be performed in 2 stages. First realistic ground truth anatomical and functional reference models of these anatomies will be created, including variation in shape and tissue properties, and including pathology (Research objective RO1).

About openGTN project - Goal and ground truth model generation

Thereafter the JEMRIS (and similar) MRI simulation software will be used to generate realistic MR images of these anatomies (RO2) and make these publicly available (RO3). More info about MR simulation with JEMRIS can be found here.

About openGTN project - Goal and MRI simulation

MRI protocol insensitive segmentation

The generated simulated MRI data will be used to optimize segmentation algorithms, especially focusin on making these algorithms as much as possible insensitive to the large variations in image appearances that are usually present in MRI (RO4).

About openGTN project - Goal and contrast insensitive MRI segmentation

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