  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.
