Automated biventricular quantification in patients with repaired tetralogy of Fallot using a 3D deep learning segmentation model-Reference-Cited by-同舟云学术

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Automated biventricular quantification in patients with repaired tetralogy of Fallot using a 3D deep learning segmentation model

Published:2024-09 Issue: Volume: Page:101092
ISSN:1097-6647
Container-title:Journal of Cardiovascular Magnetic Resonance
language:en
Short-container-title:Journal of Cardiovascular Magnetic Resonance

Author:

Tilborghs Sofie,Liang Tiffany,Raptis Stavroula,Ishikita Ayako,Budts Werner,Dresselaers Tom,Bogaert Jan,Maes Frederik,Wald Rachel M.,Van De Bruaene Alexander

Funder

Flanders Departement Economie Wetenschap en Innovatie

Canadian Institutes of Health Research

KU Leuven University Hospitals Leuven

Publisher

Elsevier BV

Reference30 articles.

1. 2018 AHA/ACC Guideline for the Management of Adults With Congenital Heart Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines;Stout;Journal of the American College of Cardiology,2019

2. 2020 ESC Guidelines for the management of adult congenital heart disease;Baumgartner;European Heart Journal,2021

3. Canadian Cardiovascular Society 2022 Guidelines for Cardiovascular Interventions in Adults With Congenital Heart Disease;Marelli;Canadian Journal of Cardiology,2022

4. Deep learning for cardiac image segmentation: A review;Chen;Frontiers in Cardiovascular Medicine,2020

5. Fully automated quantification of biventricular volumes and function in cardiovascular magnetic resonance: Applicability to clinical routine settings;Backhaus;Journal of Cardiovascular Magnetic Resonance,2019

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