Abstract
In clinical studies or population imaging settings, cardiac magnetic resonance (CMR) images may suffer from artifacts due to variability in the breath-hold position adopted by the patient during the scan. Consistent orientation of image planes with respect to the cardiac ventricles in CMR sequences forms a crucial step in the assessment of cardiac function via parameters such as the Ejection Fraction (EF) and Cardiac Output (CO) of both ventricles, which are the most immediate indicators of normal/abnormal cardiac function. In this paper, we present a novel unsupervised approach for the realistic transformation of acquired CMR images to a standard orientation using Cycle-Consistent Adversarial Networks (Cycle-GANs). We tackle this challenge by splitting the problem into two principal subtasks. First, we consider a bidirectional generator mapping between the re-oriented image and the original, hence allowing direct comparison to the input image without the need to resort to paired training data. Second, we devise a novel loss function incorporating intensity and orientation terms, and aims to produce images of high perceptual quality. Extensive experiments conducted on the CMR images in the UK Biobank dataset demonstrate that the images rendered by our model can improve the accuracy of the image derived cardiac parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging (2019)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks, pp. 1125–1134 (2017)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469–477 (2016)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Nie, D., et al.: Medical image synthesis with context-aware generative adversarial networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 417–425. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_48
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396 (2016)
Tran, P.V.: A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494 (2016)
Zhang, L., et al.: Automatic assessment of full left ventricular coverage in cardiac cine magnetic resonance imaging with fisher-discriminative 3-D CNN. IEEE Trans. Biomed. Eng. 66(7), 1975–1986 (2019)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, L. et al. (2019). Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_73
Download citation
DOI: https://doi.org/10.1007/978-3-030-32245-8_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32244-1
Online ISBN: 978-3-030-32245-8
eBook Packages: Computer ScienceComputer Science (R0)