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

Resultado final

D3.3 Data augmentation approaches for feature training or learning

D33 Data augmentation approaches for feature training or learning

D3.4 Validation of protocol insensitive MR segmentation on database with simulated MR data

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

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

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

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

D1.2 Brain and spine models with disease characteristics

D12 Brain and spine models with disease characteristics

D1.3 Heart model with functional characteristics

D13 Heart model with functional characteristics

D2.5 Evaluation of the quality of simulated MR images including cardiac function with clinical partners

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

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

D5.5 Web-access to database with simulated MR data

D55 Webaccess to database with simulated MR data

Publicaciones

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

Autores: Lustermans, Didier R. P. R. M.; Amirrajab, Sina; Veta, Mitko; Breeuwer, Marcel; Scannell, Cian M.
Publicado en: arXIV, Edición 1, 2021, ISSN 2331-8422
Editor: arXiv

Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology

Autores: Evianne Kruithof; Sina Amirrajab; Matthijs J. M. Cluitmans; Kevin D Lau; Marcel Breeuwer
Publicado en: VOLUME=137;ISSN=0010-4825;TITLE=Computers in Biology and Medicine, Edición 1, 2021, ISSN 0010-4825
Editor: 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

Autores: Evianne Kruithof; Sina Amirrajab; Kevin D. Lau; Marcel Breeuwer
Publicado en: Data in Brief, Vol 40, Iss , Pp 107691- (2022), Edición 1, 2021, ISSN 2352-3409
Editor: Elsevier BV
DOI: 10.1016/j.dib.2021.107691

XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms

Autores: 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
Publicado en: arXiv - Lecture Notes in Computer Science, Edición 1, 2020
Editor: arXiv
DOI: 10.1007/978-3-030-59719-1_13

Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation

Autores: Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer
Publicado en: Lecture Notes in Computer Science, Edición volume 13131, 2021, Página(s) 360-373
Editor: 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

Autores: Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, and M. Breeuwer
Publicado en: Lecture Notes in Computer Science, Edición volume 12417, 2020, Página(s) 68-79
Editor: SpringerLink
DOI: 10.1007/978-3-030-59520-3_8

Resultado final

Publicaciones

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