ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image RegistrationDownload PDF

Mar 23, 2021 (edited Apr 20, 2021)MIDL 2021 Conference Short SubmissionReaders: Everyone
  • Keywords: Image Registration, Vision Transformer, Convolutional Neural Networks
  • Abstract: In the last decade, convolutional neural networks (ConvNets) have dominated and achieved state-of-the-art performances in a variety of medical imaging applications. However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image. The recently proposed Vision Transformer (ViT) for image classification uses a purely self-attention-based model that learns long-range spatial relations to focus on the relevant parts of an image. Nevertheless, ViT emphasizes the low-resolution features because of the consecutive downsamplings, result in a lack of detailed localization information, making it unsuitable for image registration. Recently, several ViT-based image segmentation methods have been combined with ConvNets to improve the recovery of detailed localization information. Inspired by them, we present ViT-V-Net, which bridges ViT and ConvNet to provide volumetric medical image registration. The experimental results presented here demonstrate that the proposed architecture achieves superior performance to several top-performing registration methods.
  • Paper Type: both
  • Primary Subject Area: Image Registration
  • Secondary Subject Area: Image Registration
  • Paper Status: original work, not submitted yet
  • Source Code Url:
  • Data Set Url: The MRI brain data was acquired as part of an IRB protocol and is not approved for public release.
  • Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
4 Replies