Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks | Zendy

Ahmed S. Fahmy | Zendy; Johannes Rausch | Zendy; Ulf Neisius | Zendy; Raymond H. Chan | Zendy; Martin S. Maron | Zendy; Evan Appelbaum | Zendy; Bjoern Menze | Zendy; Reza Nezafat | Zendy

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Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks

Author(s) -

Ahmed S. Fahmy,

Johannes Rausch,

Ulf Neisius,

Raymond H. Chan,

Martin S. Maron,

Evan Appelbaum,

Bjoern Menze,

Reza Nezafat

Publication year - 2018

Publication title -

jacc. cardiovascular imaging

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 5.79

H-Index - 120

eISSN - 1936-878X

pISSN - 1876-7591

DOI - 10.1016/j.jcmg.2018.04.030

Subject(s) - hypertrophic cardiomyopathy , medicine , cardiac magnetic resonance , risk stratification , magnetic resonance imaging , cardiology , convolutional neural network , cardiac magnetic resonance imaging , cardiomyopathy , biomarker , radiology , heart failure , artificial intelligence , computer science , chemistry , biochemistry

Scar volume quantified by cardiovascular magnetic resonance (CMR) with late gadolinium enhancement (LGE) is a novel imaging biomarker for risk stratification in patients with hypertrophic cardiomyopathy (HCM) [(1)][1]. In current practice, scar quantification often relies on manual delineation of

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