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CORDIS - EU research results
CORDIS

Open Ground Truth Training Network : Magnetic resonance image simulation for training and validation of image analysis algorithms

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

D3.3 Data augmentation approaches for feature training or learning (opens in new window)

D33 Data augmentation approaches for feature training or learning

D3.4 Validation of protocol insensitive MR segmentation on database with simulated MR data (opens in new window)

D34 Validation of protocol insensitive MR segmentation on databasewith simulated MR data

D1.1 Anatomical reference models & for brain, heart & spine (opens in new window)
D2.3 Final database with highly realistic brain, spine and cardiac MR images including disease & function (opens in new window)

D23 Final database with highly realistic brain spine and cardiac MR images including disease function

D2.2 Initial database with highly realistic brain, spine and cardiac MR images for different protocols (opens in new window)

D22 Initial database with highly realistic brain spine and cardiac MR images for different protocols

D1.2 Brain and spine models with disease characteristics (opens in new window)

D12 Brain and spine models with disease characteristics

D1.3 Heart model with functional characteristics (opens in new window)

D13 Heart model with functional characteristics

D2.5 Evaluation of the quality of simulated MR images including cardiac function with clinical partners (opens in new window)

D25 Evaluation of the quality of simulated MR images including cardiac function with clinical partners

D2.4 Evaluation of the quality of simulated MR images including brain and spine disease with clinical partners (opens in new window)

D24 Evaluation of the quality of simulated MR images including brainand spine disease with clinical partners

Publications

Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation

Author(s): Lustermans, Didier R. P. R. M.; Amirrajab, Sina; Veta, Mitko; Breeuwer, Marcel; Scannell, Cian M.
Published in: arXIV, Issue 1, 2021, ISSN 2331-8422
Publisher: arXiv

Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology (opens in new window)

Author(s): Evianne Kruithof; Sina Amirrajab; Matthijs J. M. Cluitmans; Kevin D Lau; Marcel Breeuwer
Published in: VOLUME=137;ISSN=0010-4825;TITLE=Computers in Biology and Medicine, Issue 1, 2021, ISSN 0010-4825
Publisher: Pergamon Press Ltd.
DOI: 10.1016/j.compbiomed.2021.104773

Simulated late gadolinium enhanced cardiac magnetic resonance imaging dataset from mechanical XCAT phantom including a myocardial infarct (opens in new window)

Author(s): Evianne Kruithof; Sina Amirrajab; Kevin D. Lau; Marcel Breeuwer
Published in: Data in Brief, Vol 40, Iss , Pp 107691- (2022), Issue 1, 2021, ISSN 2352-3409
Publisher: Elsevier BV
DOI: 10.1016/j.dib.2021.107691

XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms (opens in new window)

Author(s): Amirrajab, Sina; Abbasi-Sureshjani, Samaneh; Khalil, Yasmina Al; Lorenz, Cristian; Weese, Juergen; Pluim, Josien; Breeuwer, Marcel; Martel, Anne L.; Abolmaesumi, Purang; Stoyanov, Danail; Mateus, Diana; Zuluaga, Maria A.; Zhou, S. Kevin; Racoceanu, Daniel; Joskowicz, Leo
Published in: arXiv - Lecture Notes in Computer Science, Issue 1, 2020
Publisher: arXiv
DOI: 10.1007/978-3-030-59719-1_13

Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation (opens in new window)

Author(s): Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer
Published in: Lecture Notes in Computer Science, Issue volume 13131, 2021, Page(s) 360-373
Publisher: SpringerLink
DOI: 10.1007/978-3-030-93722-5_39

Heterogeneous Virtual Population of Simulated CMR Images for Improving the Generalization of Cardiac Segmentation Algorithms (opens in new window)

Author(s): Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, and M. Breeuwer
Published in: Lecture Notes in Computer Science, Issue volume 12417, 2020, Page(s) 68-79
Publisher: SpringerLink
DOI: 10.1007/978-3-030-59520-3_8

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