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2017 | OriginalPaper | Chapter

Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data

Authors : Agisilaos Chartsias, Thomas Joyce, Rohan Dharmakumar, Sotirios A. Tsaftaris

Published in: Simulation and Synthesis in Medical Imaging

Publisher: Springer International Publishing

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Abstract

This paper demonstrates the potential for synthesis of medical images in one modality (e.g. MR) from images in another (e.g. CT) using a CycleGAN [24] architecture. The synthesis can be learned from unpaired images, and applied directly to expand the quantity of available training data for a given task. We demonstrate the application of this approach in synthesising cardiac MR images from CT images, using a dataset of MR and CT images coming from different patients. Since there can be no direct evaluation of the synthetic images, as no ground truth images exist, we demonstrate their utility by leveraging our synthetic data to achieve improved results in segmentation. Specifically, we show that training on both real and synthetic data increases accuracy by 15% compared to real data. Additionally, our synthetic data is of sufficient quality to be used alone to train a segmentation neural network, that achieves 95% of the accuracy of the same model trained on real data.

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next chapter Deep MR to CT Synthesis Using Unpaired Data
Footnotes
Literature
1.
Alessandrini, M., De Craene, M., Bernard, O., Giffard-Roisin, S., Allain, P., Waechter-Stehle, I., Weese, J., Saloux, E., Delingette, H., Sermesant, M.: A pipeline for the generation of realistic 3D synthetic echocardiographic sequences: methodology and open-access database. IEEE TMI 34(7), 1436–1451 (2015)
2.
3.
4.
Cordier, N., Delingette, H., Lê, M., Ayache, N.: Extended modality propagation: image synthesis of pathological cases. IEEE TMI 35(12), 2598–2608 (2016)
5.
Duchateau, N., Sermesant, M., Delingette, H., Ayache, N.: Model-based generation of large databases of cardiac images: synthesis of pathological cine MR sequences from real healthy cases. IEEE TMI (99) (2017). doi:10.​1109/​TMI.​2017.​2714343
6.
Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
7.
Huang, Y., Beltrachini, L., Shao, L., Frangi, A.F.: Geometry regularized joint dictionary learning for cross-modality image synthesis in magnetic resonance imaging. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 118–126. Springer, Cham (2016). doi:10.​1007/​978-3-319-46630-9_​12 CrossRef
8.
Huang, Y., Shao, L., Frangi, A.F.: Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding. preprint arXiv:​1705.​02596 (2017)
9.
Iglesias, J.E., Konukoglu, E., Zikic, D., Glocker, B., Van Leemput, K., Fischl, B.: Is synthesizing MRI contrast useful for inter-modality analysis? In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 631–638. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-40811-3_​79 CrossRef
10.
Jog, A., Carass, A., Roy, S., Pham, D.L., Prince, J.L.: Random forest regression for magnetic resonance image synthesis. Med. Image Anal. 35, 475–488 (2017)CrossRef
11.
12.
Oktay, O., et al.: Multi-input cardiac image super-resolution using convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 246–254. Springer, Cham (2016). doi:10.​1007/​978-3-319-46726-9_​29 CrossRef
13.
Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Guerrero, R., Cook, S., de Marvao, A., O’Regan, D.: Anatomically constrained neural networks (ACNN): application to cardiac image enhancement and segmentation. preprint arXiv:​1705.​08302 (2017)
14.
Prakosa, A., Sermesant, M., Delingette, H., Marchesseau, S., Saloux, E., Allain, P., Villain, N., Ayache, N.: Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images. IEEE TMI 32(1), 99–109 (2013)
15.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. preprint arXiv:​1511.​06434 (2015)
16.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi:10.​1007/​978-3-319-24574-4_​28 CrossRef
17.
Sevetlidis, V., Giuffrida, M.V., Tsaftaris, S.A.: Whole image synthesis using a deep encoder-decoder network. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 127–137. Springer, Cham (2016). doi:10.​1007/​978-3-319-46630-9_​13 CrossRef
18.
Tavakoli, V., Amini, A.A.: A survey of shaped-based registration and segmentation techniques for cardiac images. Comput. Vis. Image Underst. 117(9), 966–989 (2013)CrossRef
19.
20.
van Tulder, G., de Bruijne, M.: Why does synthesized data improve multi-sequence classification? In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 531–538. Springer, Cham (2015). doi:10.​1007/​978-3-319-24553-9_​65 CrossRef
21.
Van Nguyen, H., Zhou, K., Vemulapalli, R.: Cross-domain synthesis of medical images using efficient location-sensitive deep network. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 677–684. Springer, Cham (2015). doi:10.​1007/​978-3-319-24553-9_​83 CrossRef
22.
Vemulapalli, R., Van Nguyen, H., Kevin Zhou, S.: Unsupervised cross-modal synthesis of subject-specific scans. In: IEEE ICCV, pp. 630–638 (2015)
23.
Zhou, Y., Giffard-Roisin, S., De Craene, M., D’hooge, J., Alessandrini, M., Friboulet, D., Sermesant, M., Bernard, O.: A framework for the generation of realistic synthetic cardiac ultrasound and magnetic resonance imaging sequences from the same virtual patients. IEEE TMI (99) (2017). doi:10.​1109/​TMI.​2017.​2708159
24.
25.
Zhuang, X., Rhode, K.S., Razavi, R.S., Hawkes, D.J., Ourselin, S.: A registrationbased propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE TMI 29(9), 1612–1625 (2010)
26.
Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77–87 (2016)CrossRef
Metadata
Title
Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data
Authors
Agisilaos Chartsias
Thomas Joyce
Rohan Dharmakumar
Sotirios A. Tsaftaris
Copyright Year
2017
Book
Simulation and Synthesis in Medical Imaging

Print ISBN: 978-3-319-68126-9

Electronic ISBN: 978-3-319-68127-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68127-6_1

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