Titlebook: Reconstruction, Segmentation, and Analysis of Medical Images; First International Maria A. Zuluaga,Kanwal Bhatia,Danielle F. Pace Conferen

JEER

Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRIty images to be reconstructed from multiple undersampled single-shot acquisitions. The proposed method is a joint image reconstruction and motion correction method consisting of several steps, including a non-rigid motion extraction and a motion-compensated reconstruction. The reconstruction include

Occipital-Lobe

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines

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DIKE

Malcontent

Automatic Whole-Heart Segmentation in Congenital Heart Disease Using Deeply-Supervised 3D FCNork, we propose a deep learning method for automatic whole-heart segmentation in cardiac magnetic resonance (CMR) images with CHD. First, we start with a 3D fully convolutional network (3D FCN) in order to ensure an efficient voxel-wise labeling. Then we introduce dilated convolutional layers (3D-HO

织物

indoctrinate

Keshan-disease

Strengths and Pitfalls of Whole-Heart Atlas-Based Segmentation in Congenital Heart Disease Patientstive success in this domain, its implementation in whole-heart segmentation of paediatric patients suffering from a form of congenital heart disease is not straightforward. The aim of this work is to evaluate the current strengths and limitations of whole-heart atlas based segmentation techniques wi

陶器

教唆