TransMorph: Transformer for unsupervised medical image registration |
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| Institution: | 1. Department of Computer Engineering and IT, Shiraz University of Technology, Shiraz, Iran;2. Research Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran;1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;2. Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;3. Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea;1. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China;2. Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China;3. Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China;4. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, China;5. Department of Thoracic Surgery, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou 215028, China;6. The Affiliated Changzhou NO.2 People''s Hospital of Nanjing Medical University, Changzhou 213003, China;1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;2. Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China;3. Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China;4. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea |
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| Abstract: | 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. |
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| Keywords: | Image registration Deep learning Vision transformer Computerized phantom |
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