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Kerfoot, E.* ; Puyol-Antón, E.* ; Ruijsink, B.* ; Ariga, R.* ; Zacur, E.* ; Lamata, P.* ; Schnabel, J.A.*

Synthesising images and labels between mr sequence types with cycleGAN.

In: (MICCAI Workshop on Domain Adaptation and Representation Transfer). Berlin [u.a.]: Springer, 2019. 45-53 (Lect. Notes Comput. Sc. ; 11795 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Cardiac Mr ; Cardiac Quantification ; Convolutional Neural Networks ; Generative Adversarial Networks ; Image Synthesis
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel MICCAI Workshop on Domain Adaptation and Representation Transfer
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11795 LNCS, Heft: , Seiten: 45-53 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)