OpenGTN SIGNED
Open Ground Truth Training Network : Magnetic resonance image simulationfor training and validation of image analysis algorithms
Total Cost €
0EC-Contrib. €
0Partnership
0Views
0OpenGTN project word cloud
Explore the words cloud of the OpenGTN project. It provides you a very rough idea of what is the project "OpenGTN" about.
Project "OpenGTN" data sheet
The following table provides information about the project.
| Coordinator |
TECHNISCHE UNIVERSITEIT EINDHOVEN
Organization address contact info |
| Coordinator Country | Netherlands [NL] |
| Total cost | 766˙122 € |
| EC max contribution | 766˙122 € (100%) |
| Programme |
1. H2020-EU.1.3.1. (Fostering new skills by means of excellent initial training of researchers) |
| Code Call | H2020-MSCA-ITN-2017 |
| Funding Scheme | MSCA-ITN-EID |
| Starting year | 2018 |
| Duration (year-month-day) | from 2018-01-01 to 2021-12-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | TECHNISCHE UNIVERSITEIT EINDHOVEN | NL (EINDHOVEN) | coordinator | 766˙122.00 |
| 2 | Philips GmbH | DE (Hamburg) | participant | 0.00 |
Map
Project objective
This action aims to optimally prepare three young researchers for the evolving medical imaging world by offering a unique set of targeted interdisciplinary training and research assignments in the areas of anatomy, pathology, imaging techniques, quantitative image analysis and segmentation, Magnetic Resonance (MR) physics and MR image simulation. MR imaging is the major imaging modality for brain and spine anatomy and pathology. A clear trend can be observed from visual to computer-assisted diagnosis by quantification of disease-specific biomarkers, derived from the MR images. The major components in image quantification applications are tissue and organ segmentation and classification. Manual segmentation is too tedious and cumbersome for daily clinical practice and would lead to large inter-user variability. Much research is therefore performed on automatic segmentation techniques. Training, validation and benchmarking of these techniques is currently impeded by the lack of MR image databases with exact reference segmentations. The research will follow an innovative approach to overcome the current barriers for wide uptake of automatic segmentation. By combining mathematical organ models with physical and biological tissue properties and image simulation methods, substantial public image databases will be established providing ample MR images with ground truth (exact) segmentations, by which fast and accurate optimization and validation of image segmentation algorithms will be enabled. Based on sound career development plans, and coached by experienced supervisors a training is offered by leading image analysis research groups from Philips (global leader in medical imaging) and the Eindhoven University of Technology (world-wide recognized authority in education and research on image analysis, esp. on MRI) and supported by researchers from leading clinical centers as UMC Utrecht, TU Munich, Kings College London and the German Center for Neurodegenerative Diseases.
Deliverables
| D1.1 Anatomical reference models & for brain, heart & spine | Documents, reports | 2020-03-11 14:39:21 |
Take a look to the deliverables list in detail: detailed list of OpenGTN deliverables.
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "OPENGTN" project.
For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.
Send me an email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.
Thanks. And then put a link of this page into your project's website.
The information about "OPENGTN" are provided by the European Opendata Portal: CORDIS opendata.