TransMorph: Transformer for unsupervised medical image registration

By Junyu Chen, Eric C. Frey, Yufan He, W. Paul Segars, Ye Li, Yong Du. Medical Image Anal., 2021

651 citations recorded by the source corpus.

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/60d7d8a7ce2b5fffb271bb4b666537dcd1f6d16e

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In Computer Vision and Pattern Recognition

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In Neurology

  1. Munawar Hayat
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In Radiology, Nuclear Medicine and Imaging

  1. Munawar Hayat
  2. Salman Hameed Khan
  3. Huan Fu
  4. Muhammad Haris Khan
  5. Fahad Shahbaz Khan
  6. Fahad Shamshad
  7. Syed Waqas Zamir

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