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Found 20 papers, 0 papers with code
Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction using Mesh Priors
This requires analysis of the coronary lumen and plaque.
From algorithms to action: improving patient care requires causality
In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions.
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.
Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction.
Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images
Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from.
Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis
We combine our previous works for the analysis of the complete coronary artery tree and the LV myocardium: Coronary arteries are encoded by two disjoint convolutional autoencoders (CAEs) and the LV myocardium is characterized by a convolutional neural network (CNN) and a CAE.
Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography
In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen.
Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment.
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
To meet demands of the increasing interest in quantification of CAC, i. e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed.
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93. 7% with 96 manually annotated reference centerlines.
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information
We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent.
CNN-based Landmark Detection in Cardiac CTA Scans
Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches farther from it, only those patches that contain the landmark according to classification are used to determine the landmark location.
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography
The results demonstrate that automatic detection and classification of coronary artery plaque and stenosis are feasible.
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages.
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images.
Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network
Subsequently, to obtain the segmentation of the LV, voxel classification is performed within the defined bounding box using a CNN.
ConvNet-Based Localization of Anatomical Structures in 3D Medical Images
We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet).
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge.
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes.
