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

Projektinformationen

OpenGTN

ID Finanzhilfevereinbarung: 764465

DOI

10.3030/764465

Projekt abgeschlossen

EK-Unterschriftsdatum 12 Dezember 2017

Startdatum 1 Januar 2018

Enddatum 31 Dezember 2021

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 766 122,84

EU-Beitrag

€ 766 122,84

Koordiniert durch

TECHNISCHE UNIVERSITEIT EINDHOVEN
Netherlands

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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

Veröffentlichungen

Peer reviewed articles (3)

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

Autoren: Lustermans, Didier R. P. R. M.; Amirrajab, Sina; Veta, Mitko; Breeuwer, Marcel; Scannell, Cian M.
Veröffentlicht in: arXIV, Ausgabe 1, 2021, ISSN 2331-8422
Herausgeber: arXiv

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

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

Autoren: Evianne Kruithof; Sina Amirrajab; Kevin D. Lau; Marcel Breeuwer
Veröffentlicht in: Data in Brief, Vol 40, Iss , Pp 107691- (2022), Ausgabe 1, 2021, ISSN 2352-3409
Herausgeber: 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

Autoren: 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
Veröffentlicht in: arXiv - Lecture Notes in Computer Science, Ausgabe 1, 2020
Herausgeber: arXiv
DOI: 10.1007/978-3-030-59719-1_13

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

Autoren: Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, J. Pluim, M. Breeuwer
Veröffentlicht in: Lecture Notes in Computer Science, Ausgabe volume 13131, 2021, Seite(n) 360-373
Herausgeber: 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

Autoren: Y. Al Khalil, S. Amirrajab, C. Lorenz, J. Weese, and M. Breeuwer
Veröffentlicht in: Lecture Notes in Computer Science, Ausgabe volume 12417, 2020, Seite(n) 68-79
Herausgeber: SpringerLink
DOI: 10.1007/978-3-030-59520-3_8