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2018

Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN

Yang, Heran, Sun, Jian, Carass, Aaron et al.

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The cycleGAN is becoming an influential method in medical image synthesis.

However, due to a lack of direct constraints between input and synthetic images, the cycleGAN cannot guarantee structural consistency between these two images, and such consistency is of extreme importance in medical imaging.
To overcome this, we propose a structure-constrained cycleGAN for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neighborhood descriptor to constrain structural consistency.
Additionally, we use a position-based selection strategy for selecting training images instead of a completely random selection scheme.

Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN · Around

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Hofmann, M., Bezrukov, I., et al.: MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and atlas-based methods. J. Nucl. Med. 52
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Heinrich, M.P., et al.: MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16
2012
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Goodfellow, I., et al.: Generative adversarial nets. In: NIPS. pp. 2672–2680 (2014)
2014
Earlier work this paper cites.

Similar

Chartsias, A., Joyce, T., et al.: Adversarial image synthesis for unpaired multi-modal cardiac data. In: SASHIMI. pp. 3–13 (2017)
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Roy, S., Butman, J.A., Pham, D.L.: Synthesizing CT from ultrashort echo-time MR images via convolutional neural networks. In: SASHIMI. pp. 24–32 (2017)
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Wolterink, J.M., Dinkla, A.M., et al.: Deep MR to CT synthesis using unpaired data. In: SASHIMI. pp. 14–23 (2017)
2017
Cited alongside, same era.

Then

Zhu, J.Y., Park, T., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV. pp. 2242–2251 (2017)
2017
Later among the works it cites.
Zhang, Z., et al.: Translating and segmenting multimodal medical volumes with cycle- and shape-consistency generative adversarial network. In: CVPR (2018)
2018
Closest in time.

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