BIRNet: Brain image registration using dual-supervised fully convolutional networks

Jingfan Fan, Xiaohuan Cao, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

180 Citations (Scopus)


In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Ground-truth guidance using deformation fields obtained by an existing registration method; and (2) Image dissimilarity guidance using the difference between the images after registration. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.

Original languageEnglish
Pages (from-to)193-206
Number of pages14
JournalMedical Image Analysis
Publication statusPublished - 2019 May


  • Brain MR image
  • Convolutional neural networks
  • Hierarchical registration
  • Image registration

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'BIRNet: Brain image registration using dual-supervised fully convolutional networks'. Together they form a unique fingerprint.

Cite this