Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-d...
Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model
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England: Elsevier Inc
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English
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England: Elsevier Inc
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Deep learning is the state-of-the-art approach for automated segmentation of the left ventricle (LV) and right ventricle (RV) in cardiovascular magnetic resonance (CMR) images. However, these models have been mostly trained and validated using CMR datasets of structurally normal hearts or cases with acquired cardiac disease, and are therefore not w...
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Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model
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TN_cdi_doaj_primary_oai_doaj_org_article_68cbf1ae0e08485fb08ce226d72fd284
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_68cbf1ae0e08485fb08ce226d72fd284
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ISSN
1097-6647,1532-429X
E-ISSN
1532-429X
DOI
10.1016/j.jocmr.2024.101092
