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Found 5 papers, 1 papers with code
Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy
The evaluation of the method is performed on a dataset of cine CMRI scans from 47 ARVC patients and 67 controls.
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.
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.
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.
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