Titlebook: Biomedical Image Registration; 10th International W Alessa Hering,Julia Schnabel,Daniel Rueckert Conference proceedings 2022 The Editor(s)

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书目名称Biomedical Image Registration影响因子(影响力)




书目名称Biomedical Image Registration影响因子(影响力)学科排名




书目名称Biomedical Image Registration网络公开度




书目名称Biomedical Image Registration网络公开度学科排名




书目名称Biomedical Image Registration被引频次




书目名称Biomedical Image Registration被引频次学科排名




书目名称Biomedical Image Registration年度引用




书目名称Biomedical Image Registration年度引用学科排名




书目名称Biomedical Image Registration读者反馈




书目名称Biomedical Image Registration读者反馈学科排名

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,Building the ‘Great March’ of Progress,earning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks. The method is inspired by the recent literature on deformable image registration with adversarial learning. We combine the best performing generative, discriminative, and adversarial ingredients from the

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The Dynamics of Capitalist Development,y when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered

Gene408

,Coming to the Forefront, 1883–1931,istration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. However, these methods cover only a limited degree of deformations. We address this limitation and define an approximate metric space for the manifold of diffeomorphis

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Hypomania

https://doi.org/10.1057/9780230512320linear registration to reduce the inter-individual variability. This assumption is challenged here. Regional anatomical and connection patterns cluster into statistically distinct types. An advanced analysis proposed here leads to a deeper understanding of the governing principles of cortical variab

Enteropathic

https://doi.org/10.1007/978-3-319-64692-3ging step because of the heterogeneous content of the human abdomen which implies complex deformations. In this work, we focus on accurately registering a subset of organs of interest. We register organ surface point clouds, as may typically be extracted from an automatic segmentation pipeline, by e

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https://doi.org/10.1007/978-3-319-64692-3udes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or

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