Deep learning-based segmentation and strain analysis of left heart chambers from long-axis CMR images

Generalizable DL-based LV and LA segmentation on LAX-CMR images was proposed. Its combination with FT resulted in fully automated and reliable LV and LA strain measures, reaching human reproducibility.

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Publication Details

Journal
European Heart Journal - Imaging Methods and Practice
Published
2024-10-01
DOI
https://doi.org/10.1093/ehjimp/qyaf070
Citations
1
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
0.40

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article

Deep learning-based segmentation and strain analysis of left heart chambers from long-axis CMR images

Nadjia Kachenoura, Alban Redheuil, Gilles Montalescot, Khaoula Bouazizi et al.
1 citations
European Heart Journal - Imaging Methods and Practice
Cardiac Imaging and Diagnostics
0.40
article

Deep learning-based segmentation and strain analysis of left heart chambers from long-axis CMR images

Nadjia Kachenoura, Alban Redheuil, Gilles Montalescot, Khaoula Bouazizi, Jérôme Lamy, Thomas Dietenbeck, Thomas Wallet, Vincent Nguyen, Émilie Bollache, Mikaël Prigent, Moussa Gueda Moussa, Michel Zeitouni, Mohamed Zarai, Antonio Gallo, Niki Procopi, Perrine Marsac, Elie Mousseaux, Jonas Leite
article en
1 citations

Abstract

Generalizable DL-based LV and LA segmentation on LAX-CMR images was proposed. Its combination with FT resulted in fully automated and reliable LV and LA strain measures, reaching human reproducibility.

European Heart Journal - Imaging Methods and PracticeVol. 2(4)
Centre National de la Recherche Scientifique (FR), Inserm (FR), Université Paris Cité (FR), Sorbonne Université (FR), Assistance Publique – Hôpitaux de Paris (FR), Laboratoire d’Imagerie Biomédicale (FR), Pitié-Salpêtrière Hospital (FR), Paris Cardiovascular Research Center (FR), European Hospital (IT), ACTION (US), Fondation pour l’innovation en Cadiométabolisme et Nutrition (FR)
Horizon 2020 Framework Programme
Openalex Percentile: Top 30%
Cardiac Imaging and Diagnostics
0.40
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