Abstract
Accurate and reproducible quantification of cardiac magnetic resonance imaging (CMR) late gadolinium enhancement (LGE) scar volume is essential in managing patients with hypertrophic cardiomyopathy (HCM) due to the importance of scar burden in predicting clinical outcomes. In current practice, LGE quantification is subjective, time-consuming, and requires extensive training to delineate both the myocardial borders and the hyper-enhanced regions on the LGE images. Machine learning algorithms have the potential to improve scar quantification from CMR LGE images. In HCM patients, we aimed to develop novel machine learning methods that could contour the left ventricle (LV) endo- and epicardial borders and to quantify the LGE within those borders efficiently and accurately.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
