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Found 19 papers, 1 papers with code
Generative Models for Reproducible Coronary Calcium Scoring
Interscan reproducibility was compared to clinical calcium scoring in radiotherapy treatment planning CTs of 1, 662 patients, each having two scans.
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI
Given the unsupervised nature of the method, high-resolution training data is not required and hence, the method can be readily applied in clinical settings.
AI for Calcium Scoring
Calcium scoring, a process in which arterial calcifications are detected and quantified in CT, is valuable in estimating the risk of cardiovascular disease events.
Diffeomorphic Spatial Transformer Networks
In this paper we propose a spatial transformer network where the spatial transformations are limited to the group of diffeomorphisms.
Automatic segmentation with detection of local segmentation failures in cardiac MRI
The experiments reveal that combining automatic segmentation with simulated manual correction of detected segmentation failures leads to statistically significant performance increase.
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples
Instead, lower resolution images are upsampled to higher resolution using conventional interpolation methods.
Deep Group-wise Variational Diffeomorphic Image Registration
Deep neural networks are increasingly used for pair-wise image registration.
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.
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.
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
A constrained volume growing method uses these manually placed seed points as input and generates a tumor segmentation.
Automatic Segmentation of Thoracic Aorta Segments in Low-Dose Chest CT
Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement.
Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
Current state-of-the-art deep learning segmentation methods have not yet made a broad entrance into the clinical setting in spite of high demand for such automatic methods.
A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration.
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.
Direct and Real-Time Cardiovascular Risk Prediction
The results demonstrate that real-time quantification of CAC burden in chest CT without the need for segmentation of CAC is possible.
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
In this work we propose a deep learning network for deformable image registration (DIRNet).
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).
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.
