Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan. Yang, H., Sun, J., Carass, A., Zhao, C., Lee, J., Xu, Z., & Prince, J. Volume 11045 LNCS , 2018. ISSN: 16113349 Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) _eprint: 1809.04536
doi  abstract   bibtex   
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. Experimental results on synthesizing CT images from brain MR images demonstrate that our method is better than the conventional cycleGAN and approximates the cycleGAN trained with paired data.
@book{yang_unpaired_2018,
	title = {Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan},
	volume = {11045 LNCS},
	isbn = {978-3-030-00888-8},
	abstract = {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. Experimental results on synthesizing CT images from brain MR images demonstrate that our method is better than the conventional cycleGAN and approximates the cycleGAN trained with paired data.},
	author = {Yang, Heran and Sun, Jian and Carass, Aaron and Zhao, Can and Lee, Junghoon and Xu, Zongben and Prince, Jerry},
	year = {2018},
	doi = {10.1007/978-3-030-00889-5_20},
	note = {ISSN: 16113349
Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
\_eprint: 1809.04536},
	keywords = {\#nosource, CycleGAN, Deep learning, MIND, MR-to-CT synthesis},
}
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