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

Informazioni relative al progetto

OpenGTN

ID dell’accordo di sovvenzione: 764465

DOI

10.3030/764465

Progetto chiuso

Data della firma CE 12 Dicembre 2017

Data di avvio 1 Gennaio 2018

Data di completamento 31 Dicembre 2021

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 766 122,84

Contributo UE

€ 766 122,84

Coordinato da

TECHNISCHE UNIVERSITEIT EINDHOVEN
Netherlands

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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

Pubblicazioni

Peer reviewed articles (3)

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

Autori: Lustermans, Didier R. P. R. M.; Amirrajab, Sina; Veta, Mitko; Breeuwer, Marcel; Scannell, Cian M.
Pubblicato in: arXIV, Numero 1, 2021, ISSN 2331-8422
Editore: arXiv

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

Autori: Evianne Kruithof; Sina Amirrajab; Matthijs J. M. Cluitmans; Kevin D Lau; Marcel Breeuwer
Pubblicato in: VOLUME=137;ISSN=0010-4825;TITLE=Computers in Biology and Medicine, Numero 1, 2021, ISSN 0010-4825
Editore: 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

Autori: Evianne Kruithof; Sina Amirrajab; Kevin D. Lau; Marcel Breeuwer
Pubblicato in: Data in Brief, Vol 40, Iss , Pp 107691- (2022), Numero 1, 2021, ISSN 2352-3409
Editore: 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

Autori: 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
Pubblicato in: arXiv - Lecture Notes in Computer Science, Numero 1, 2020
Editore: arXiv
DOI: 10.1007/978-3-030-59719-1_13

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

Autori: Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer
Pubblicato in: Lecture Notes in Computer Science, Numero volume 13131, 2021, Pagina/e 360-373
Editore: 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

Autori: Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, and M. Breeuwer
Pubblicato in: Lecture Notes in Computer Science, Numero volume 12417, 2020, Pagina/e 68-79
Editore: SpringerLink
DOI: 10.1007/978-3-030-59520-3_8