The first version of the openGTN public database with simulated and synthesized MRI data will be made available on 31 December 2020. The database will contain cardiac, brain and spine data without pathology. Data with pathology is planned for March 2021. More information about the database can be found here.
openGTN on the MICCAI 2020 Daily

You can find the interview here,

openGTN at MICCAI 2020
The openGTN project is well represented at the MICCAI 2020 conference with one oral presentation and a contribution to the SASHIMI workshop. Details on the publications can be found here.

4 abstracts accepted for ESMRMB 2020
Four abstracts have been accepted for presentation at the online conference of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB), September 30 – October 2, 2020.
The abstract titles can be found here.

5 abstracts accepted for ISMRM 2020
Five abstracts have been accepted for poster presentation at the 28th Annual Meeting of the International Society of Magnetic Resonance in Medicine (ISMRM). The meeting was supposed to take place in May 2020 in Sydney, Australia, but was rescheduled to 8-13 August in Paris, France. The abstract titles can be found here.

Paper accepted by the prestigious MIDL 2020 conference
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Available on Openreview and arXiv.
We are thrilled to share with you our recent paper accepted for @midl_conference 2020 presenting a novel hybrid controllable image generation method for synthesizing anatomically meaningful 3D+t labelled CMR images. Check out how we efficiently combine a physics-driven anatomical model with a SOTA data-driven generative adversarial network to generate realistic-looking cardiac MR images. As a part of the OpenGTN research, we step towards tackling the limited availability of medical data for deep learning training.
openGTN workshop cancelled due to outbreak Corona virus
With much regret we have to inform you that we have to cancel the openGTN Workshop planned for 27 March 2020. Especially in the province of Noord-Brabant, in which Eindhoven is located, we have a significant outbreak of the Corona virus. As a consequence, Dutch government has recommended to work as much as possible at home and avoid unnecessary contacts. We hope we can reschedule the workshop before Summer 2020, and we hope that you will then still be interested to attend.
Last opportunity to register for the public workshop on “Realism in MRI simulation for deep learning training”, 27 March 2020 at TU/e
The openGTN public workshop on “Realism in MRI simulation for deep learning training” attracts a lot of attention. There are only 10 free seats left! The registration is soon expected to close. More information and a registration form can be found here.
Public workshop “Realistic MR image simulation for deep learning training” on 27 March 2020
The openGTN project organizes its first public workshop on the theme “Realistic magnetic resonance image simulation” on 27 March 2020 at the Eindhoven University of Technology, The Netherlands.
The three openGTN early-stage researchers will present the approaches investigated so far to generate realistic simulated MRI data and will discuss the use of these data to improve deep-learning based MRI segmentation. Thereafter three renowned, invited speakers (prof. Stoecker, prof. Rueckert and prof. Kozerke) will give their view on the subject, from different perspectives. The workshop will close with a discussion about how to make MRI simulations even more realistic.
Workshop attendance is free of charge, but registration is required (note: limited seating available!). More information and a registration form can be found here.
Scientific dissemination started!
The first openGTN project results will be presented at the ISMRM Benelux 2020 scientific conference, 24 January 2020, Arnhem, The Netherlands.
Three presentations will be given:
“Improving the generalization capability of deep learning-based algorithms for ventricular cavity segmentation using simulated cardiac MR images”
(oral presentation by Yasmina Al Khalil)
“Towards generating realistic and heterogeneous cardiac MR simulated image database for deep learning based image segmentation algorithms”
(poster presentation by Sina Amirrajab)
“Pipeline for Simulating Realistic Anatomically Variable Normal Young, Aging and Diseased Brain MRI”
(poster presentation by Aymen Ayaz)