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

Informations projet

OpenGTN

N° de convention de subvention: 764465

DOI

10.3030/764465

Projet clôturé

Date de signature de la CE 12 Decembre 2017

Date de début 1 Janvier 2018

Date de fin 31 Decembre 2021

Financé au titre de

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coût total

€ 766 122,84

Contribution de l’UE

€ 766 122,84

Coordonné par

TECHNISCHE UNIVERSITEIT EINDHOVEN
Netherlands

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (9)

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

Other (1)

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

D55 Webaccess to database with simulated MR data

Publications

Peer reviewed articles (3)

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

Auteurs: Lustermans, Didier R. P. R. M.; Amirrajab, Sina; Veta, Mitko; Breeuwer, Marcel; Scannell, Cian M.
Publié dans: arXIV, Numéro 1, 2021, ISSN 2331-8422
Éditeur: arXiv

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

Auteurs: Evianne Kruithof; Sina Amirrajab; Matthijs J. M. Cluitmans; Kevin D Lau; Marcel Breeuwer
Publié dans: VOLUME=137;ISSN=0010-4825;TITLE=Computers in Biology and Medicine, Numéro 1, 2021, ISSN 0010-4825
Éditeur: 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

Auteurs: Evianne Kruithof; Sina Amirrajab; Kevin D. Lau; Marcel Breeuwer
Publié dans: Data in Brief, Vol 40, Iss , Pp 107691- (2022), Numéro 1, 2021, ISSN 2352-3409
Éditeur: Elsevier BV
DOI: 10.1016/j.dib.2021.107691

Book chapters (3)

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

Auteurs: 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
Publié dans: arXiv - Lecture Notes in Computer Science, Numéro 1, 2020
Éditeur: arXiv
DOI: 10.1007/978-3-030-59719-1_13

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

Auteurs: Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer
Publié dans: Lecture Notes in Computer Science, Numéro volume 13131, 2021, Page(s) 360-373
Éditeur: 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

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