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Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study
Convolutional neural networks (CNNs) have demonstrated promise in automa...
12/31/2020 ∙ by Matthew Ng, et al. ∙
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Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank
Purpose: To demonstrate the feasibility and performance of a fully autom...
04/15/2020 ∙ by Edward Ferdian, et al. ∙
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Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Accelerating the acquisition of magnetic resonance imaging (MRI) is a ch...
08/20/2019 ∙ by Chen Qin, et al. ∙
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3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata
Large prospective epidemiological studies acquire cardiovascular magneti...
07/02/2019 ∙ by Rahman Attar, et al. ∙
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Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Background: The trend towards large-scale studies including population i...
01/27/2019 ∙ by Robert Robinson, et al. ∙
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High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
The exploitation of large-scale population data has the potential to imp...
01/10/2019 ∙ by Rahman Attar, et al. ∙
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Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN
Cardiac magnetic resonance (CMR) images play a growing role in the diagn...
11/06/2018 ∙ by Le Zhang, et al. ∙
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Real-time Prediction of Segmentation Quality
Recent advances in deep learning based image segmentation methods have e...
06/16/2018 ∙ by Robert Robinson, et al. ∙
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Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Cardiac motion estimation and segmentation play important roles in quant...
06/11/2018 ∙ by Chen Qin, et al. ∙
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Human-level CMR image analysis with deep fully convolutional networks
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging mo...
10/25/2017 ∙ by Wenjia Bai, et al. ∙
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