no code implementations • 3 Jun 2022 • Ariel H. Curiale, MatÍas E. Calandrelli, Lucca Dellazoppa, Mariano Trevisan, Jorge Luis BociÁn, Juan Pablo Bonifacio, GermÁn Mato
Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.
no code implementations • 14 Dec 2018 • Ariel H. Curiale, Flavio D. Colavecchia, German Mato
Significance: This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring.
no code implementations • 24 Aug 2017 • Ariel H. Curiale, Flavio D. Colavecchia, Pablo Kaluza, Roberto A. Isoardi, German Mato
In this work we propose to use a deep learning technique to assist the automatization of myocardial segmentation in cardiac MRI.