Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN

Heran Yang, Jian Sun, Aaron Carass, Can Zhao, Junghoon Lee, Zongben Xu, Jerry Prince

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Sep 12 2018


  • CycleGAN
  • Deep learning
  • MIND
  • MR-to-CT synthesis

ASJC Scopus subject areas

  • General

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