Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives.
atrial fibrillation
cardiac magnetic resonance
deep learning
late gadolinium enhancement
myocardial infarction
scar segmentation
Journal
Frontiers in physiology
ISSN: 1664-042X
Titre abrégé: Front Physiol
Pays: Switzerland
ID NLM: 101549006
Informations de publication
Date de publication:
2021
2021
Historique:
received:
13
05
2021
accepted:
28
06
2021
entrez:
20
8
2021
pubmed:
21
8
2021
medline:
21
8
2021
Statut:
epublish
Résumé
Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.
Identifiants
pubmed: 34413789
doi: 10.3389/fphys.2021.709230
pmc: PMC8369509
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
709230Subventions
Organisme : British Heart Foundation
ID : PG/16/78/32402
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/19/1/34160
Pays : United Kingdom
Organisme : British Heart Foundation
ID : TG/18/5/34111
Pays : United Kingdom
Informations de copyright
Copyright © 2021 Wu, Tang, Li, Firmin and Yang.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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