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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022; 25th International C Linwei Wang,Qi Dou,Shuo Li Conference procee

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SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRIing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
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Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-Basistent with that of the student model. More importantly, instead of searching for a fixed weight, the teacher enables automatically adjusting the weights of the spatial regularization and the temporal consistency regularization by taking advantage of the transformation uncertainty and appearance unc
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On the Dataset Quality Control for Image Registration Evaluationtasets, we identified and confirmed a small number of landmarks with potential localization errors and found that, in some cases, the landmark distribution was not ideal for an unbiased assessment of non-rigid registration errors. Under discussion, we provide some constructive suggestions for improv
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Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CTinvestigated before. In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived .-maps. DuSFE fuses the knowledge from multiple modalities to recalibrate both channel-wise and spatial features for each modality. DuSFE can be
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Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning
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