Open Ground Truth Training Network – Simulated MRI for medical image analysis research
After a secondment of more than 2 years at Philips Research Hamburg, Germany, PhD researcher Yasmina Al Khalil returned to the Eindhoven University of Technology (TU/e), The Netherlands, to finalize her PhD thesis. The above picture shows the 3 openGTN PhD’s united in their room at TU/e.
Finally, after 2 years of repeated COVID lock-downs and attending online-only conferences, the 3 openGTN PhD researchers Aymen Ayaz, Yasmina Al Khalil and Sina Amirrajab could all attend and present their achievements at the ISMRM 2022 conference. The left picture shows the team at the ISMRM exhibit booth, together with prof. Marcel Breeuwer.
6 abstracts have been accepted for presentation at the coming ISMRM 2022 conference:
S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer. “Intra- and intersubject synthesis of cardiac MR images using a VAE and GAN”
S. Amirrajab, Y. Al Khalil, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer. “sim2real: Cardiac MR image simulation-to-real translation via unsupervised GANs”
Y. Al Khalil, A. Ayaz, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer. “Reducing the impact of texture on deep-learning brain tissue segmentation networks trained with simulated MR images”
Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer, “Late feature fusion and GAN-based augmentation for generalizable cardiac MRI segmentation”
A. Ayaz, K. Lukassen, C. Lorenz, J. Weese, M. Breeuwer. “Brain MR image super resolution using simulated data to perform in real-world MRI”
A. Ayaz, R. Jong, S. Abbasi-Sureshjani, S. Amirrajab, C. Lorenz, J. Weese, M. Breeuwer. “3D brain MRI synthesis utilizing 2D SPADE-GAN and 3D CNN architecture”
See also our publication list
We have recently added simulated MRI with pathology to our openGTN public database. Please visit opengtn.eu/database for more information.
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.
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.
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.
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.
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.