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- W4225564019 abstract "Deformable image registration is able to achieve fast and accurate alignment between a pair of images and thus plays an important role in many medical image studies. The current deep learning (DL)-based image registration approaches directly learn the spatial transformation from one image to another by leveraging a convolutional neural network, requiring ground truth or similarity metric. Nevertheless, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within images. Moreover, DL-based methods often estimate global spatial transformations of image directly, which never pays attention to region spatial transformations of ROIs within images. In this paper, we present a novel dual-flow transformation network with region consistency constraint which maximizes the similarity of ROIs within a pair of images and estimates both global and region spatial transformations simultaneously. Experiments on four public 3D MRI datasets show that the proposed method achieves the best registration performance in accuracy and generalization compared with other state-of-the-art methods." @default.
- W4225564019 created "2022-05-05" @default.
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- W4225564019 date "2021-12-04" @default.
- W4225564019 modified "2023-09-28" @default.
- W4225564019 title "Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint" @default.
- W4225564019 hasPublicationYear "2021" @default.
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