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Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water inExpand
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Nonrigid registration using free-form deformations: application to breast MR images
In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. TheExpand
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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In theseExpand
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Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
HIGHLIGHTSAn efficient 11‐layers deep, multi‐scale, 3D CNN architecture.A novel training strategy that significantly boosts performance.The first employment of a 3D fully connected CRF forExpand
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Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics
There is much interest in using magnetic resonance diffusion imaging to provide information on anatomical connectivity in the brain by measuring the diffusion of water in white matter tracts. AmongExpand
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Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functionalExpand
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Automatic anatomical brain MRI segmentation combining label propagation and decision fusion
Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirementsExpand
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Attention U-Net: Learning Where to Look for the Pancreas
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn toExpand
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Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy
Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of theExpand
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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade ofExpand
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