Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

  • Boah Kim
  • Jieun Kim
  • June-Goo Lee
  • Dong Hwan Kim
  • Seong Ho Park
  • Jong Chul YeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.


Deep learning Medical image registration Unsupervised learning Cycle consistency 



This work was supported by the Industrial Strategic technology development program (10072064, Development of Novel Artificial Intelligence Technologies To Assist Imaging Diagnosis of Pulmonary, Hepatic, and Cardiac Diseases and Their Integration into Commercial Clinical PACS Platforms) funded by the Ministry of Trade Industry and Energy (MI, Korea).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Korea Advanced Institute of Science and TechnologyDaejeonSouth Korea
  2. 2.Asan Medical CenterUniversity of Ulsan College of MedicineSeoulSouth Korea

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