TY - GEN
T1 - Synthesising images and labels between mr sequence types with cycleGAN
AU - Kerfoot, Eric
AU - Puyol-Antón, Esther
AU - Ruijsink, Bram
AU - Ariga, Rina
AU - Zacur, Ernesto
AU - Lamata, Pablo
AU - Schnabel, Julia
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Real-time (RT) sequences for cardiac magnetic resonance imaging (CMR) have recently been proposed as alternatives to standard cine CMR sequences for subjects unable to hold the breath or suffering from arrhythmia. RT image acquisitions during free breathing produce comparatively poor quality images, a trade-off necessary to achieve the high temporal resolution needed for RT imaging and hence are less suitable in the clinical assessment of cardiac function. We demonstrate the application of a CycleGAN architecture to train autoencoder networks for synthesising cine-like images from RT images and vice versa. Applying this conversion to real-time data produces clearer images with sharper distinctions between myocardial and surrounding tissues, giving clinicians a more precise means of visually inspecting subjects. Furthermore, applying the transformation to segmented cine data to produce pseudo-real-time images allows this label information to be transferred to the real-time image domain. We demonstrate the feasibility of this approach by training a U-net based architecture using these pseudo-real-time images which can effectively segment actual real-time images.
AB - Real-time (RT) sequences for cardiac magnetic resonance imaging (CMR) have recently been proposed as alternatives to standard cine CMR sequences for subjects unable to hold the breath or suffering from arrhythmia. RT image acquisitions during free breathing produce comparatively poor quality images, a trade-off necessary to achieve the high temporal resolution needed for RT imaging and hence are less suitable in the clinical assessment of cardiac function. We demonstrate the application of a CycleGAN architecture to train autoencoder networks for synthesising cine-like images from RT images and vice versa. Applying this conversion to real-time data produces clearer images with sharper distinctions between myocardial and surrounding tissues, giving clinicians a more precise means of visually inspecting subjects. Furthermore, applying the transformation to segmented cine data to produce pseudo-real-time images allows this label information to be transferred to the real-time image domain. We demonstrate the feasibility of this approach by training a U-net based architecture using these pseudo-real-time images which can effectively segment actual real-time images.
KW - Cardiac MR
KW - Cardiac quantification
KW - Convolutional neural networks
KW - Generative adversarial networks
KW - Image synthesis
UR - http://www.scopus.com/inward/record.url?scp=85075699776&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33391-1_6
DO - 10.1007/978-3-030-33391-1_6
M3 - Conference contribution
AN - SCOPUS:85075699776
SN - 9783030333904
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 45
EP - 53
BT - Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings
A2 - Wang, Qian
A2 - Milletari, Fausto
A2 - Rieke, Nicola
A2 - Nguyen, Hien V.
A2 - Roysam, Badri
A2 - Albarqouni, Shadi
A2 - Cardoso, M. Jorge
A2 - Xu, Ziyue
A2 - Kamnitsas, Konstantinos
A2 - Patel, Vishal
A2 - Jiang, Steve
A2 - Zhou, Kevin
A2 - Luu, Khoa
A2 - Le, Ngan
PB - Springer
T2 - 1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -