Keywords: Machine Learning/Artificial Intelligence, BrainDeformable image registration is a crucial part of the medical image process that seeks to estimate an optimal spatial transformation to align two images. Traditional methods neglect the inverse-consistent property and topology preservation of the transformation, which can lead to errors in registration results. To address these issues, we propose the cycle inverse consistent transformer-based deformable medical image registration model. We adopt Swin-UNet to achieve higher registration performance and comprehensively consider the inverse consistency loss function to guarantee a more accurate registration. Our pipeline can be trained to create study-specific templates of images for diagnostic and/or educational purposes.
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