Titlebook: Biomedical Image Registration; 11th International W Marc Modat,Ivor Simpson,Tony C. W. Mok Conference proceedings 2024 The Editor(s) (if ap

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A Learning-Free Approach to?Mitigate Abnormal Deformations in?Medical Image Registrationhave demonstrated reasonable accuracy, they can produce abnormal deformations that introduce substantial artifacts in medical images by unrealistically modifying the shape and position of anatomical structures. These abnormal deformations may not be effectively detected or mitigated during inference

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Large Deformation Registration with?A Confidence-Guided Networkattention registration part to register the image with the proper receptive field. Comprehensive experimental results on the brain and liver datasets show that our proposed confidence-guided network significantly improves registration accuracy over existing methods for large deformation registration

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Feedback Attention for?Unsupervised Cardiac Motion Estimation in?3D Echocardiographyrate its good performance in both adult and fetal echocardiography images. We introduce a novel feedback spatial transformer module where the registration outputs are used to generate a co-attention map that describes the remaining registration errors to guide the network’s spatial emphasis during D

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Learning Deformable Intra-Patient Liver Registration with?Graph Cross-Attentions-attention. Our proposed module is a novel solution that can enhance the performance of various encoder-decoder architectures. To the best of our knowledge, this is the first application of a graph cross-attention mechanism for liver registration. We carried out the experimental validation on 20 pr

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