Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis

no code implementations • 28 Oct 2021 • Avan Suinesiaputra, Charlene A Mauger, Bharath Ambale-Venkatesh, David A Bluemke, Josefine Dam Gade, Kathleen Gilbert, Mark Janse, Line Sofie Hald, Conrad Werkhoven, Colin Wu, Joao A Lima, Alistair A Young

With the advent of large-scale cardiac imaging data, statistical atlases become a powerful tool to provide automated and precise quantification of the status of patient-specific heart geometry with respect to reference populations.

Transfer Learning

Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)

no code implementations • 3 Mar 2021 • Vivek P. Jani, Nadjia Kachenoura, Alban Redheuil, Gisela Teixido-Tura, Kevin Bouaou, Emilie Bollache, Elie Mousseaux, Alain De Cesare, Shelby Kutty, Colin O. Wu, David A. Bluemke, Joao A. C. Lima, Bharath Ambale-Venkatesh

CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0. 97 and 0. 95, respectively, on the internal test set and 0. 997 and 0. 995, respectively, for the external test.

Segmentation