A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.
Adults and Pediatrics
Cardiac
Congenital
MR Imaging
Quantification
Segmentation
Volume Analysis
Journal
Radiology. Artificial intelligence
ISSN: 2638-6100
Titre abrégé: Radiol Artif Intell
Pays: United States
ID NLM: 101746556
Informations de publication
Date de publication:
Jan 2024
Jan 2024
Historique:
medline:
4
1
2024
pubmed:
3
1
2024
entrez:
2
1
2024
Statut:
ppublish
Résumé
Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods This retrospective study used 250 cardiac MRI examinations (November 2007-December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two U-Net 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (
Identifiants
pubmed: 38166332
doi: 10.1148/ryai.230132
pmc: PMC10831511
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e230132Subventions
Organisme : Medical Research Council
ID : MR/S032290/1
Pays : United Kingdom
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