39 citations · 40 across the 4 of their papers we have counts for
4 papers
Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach
Ariel H. Curiale, MatÍas E. Calandrelli, Lucca Dellazoppa +4
Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fracti…
Detection of Fibrosis in Cine Magnetic Resonance Images Using Artificial Intelligence Techniques
Ariel. H. Curiale, Facundo Cabrera, Pablo Jimenez +3
Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these…
Automatic quantification of the LV function and mass: a deep learning approach for cardiovascular MRI
Ariel H. Curiale, Flavio D. Colavecchia, German Mato
Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN). Methods: The general framework consists…
Automatic Myocardial Segmentation by Using A Deep Learning Network in Cardiac MRI
Ariel H. Curiale, Flavio D. Colavecchia, Pablo Kaluza +2
Cardiac function is of paramount importance for both prognosis and treatment of different pathologies such as mitral regurgitation, ischemia, dyssynchrony and myocarditis. Cardiac…