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: https://github.com/junyuchen245/ViT-V-Net_for_3D_Image_Registration
Data Set Url: The MRI brain data was acquired as part of an IRB protocol and is not approved for public release.
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