Deep learning-based segmentation and strain analysis of left heart chambers from long-axis CMR images Article Swipe
Jonas Leite
,
Émilie Bollache
,
Vincent Nguyen
,
Moussa Gueda Moussa
,
Thomas Wallet
,
Mikaël Prigent
,
Khaoula Bouazizi
,
Mohamed Zarai
,
Jérôme Lamy
,
Perrine Marsac
,
Niki Procopi
,
Michel Zeitouni
,
Alban Redheuil
,
Antonio Gallo
,
Elie Mousseaux
,
Gilles Montalescot
,
Thomas Dietenbeck
,
Nadjia Kachenoura
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/ehjimp/qyaf070
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/ehjimp/qyaf070
Aims Feature tracking (FT) is increasingly used on dynamic cardiac magnetic resonance (CMR) images for myocardial strain evaluation but often requires manual initialization, which is tedious and source of variability, especially on the challenging long-axis (LAX) images. Accordingly, we designed a pipeline combining deep learning (DL) with FT for left ventricular (LV) and left atrial (LA) longitudinal myocardial strain estimation. Methods and results We studied a multivendor database of 684 individuals divided into: training = 845, tuning = 281, and testing = 116 LAX-CMR cine 2- and/or 4-chamber views. Images were centre cropped. Then, a 2D- and 3D-ResUnet, which considers time as the third dimension, were designed for LV/LA segmentation and used to (i) estimate LV and LA strains (Full 2D−/3D-DL) and (ii) initialize an FT algorithm and further derive LV and LA strains (FT-initialized by 2D−/3D-DL). Left ventricular and LA contours and strain peaks were compared against reference standard (RS) measures performed by an expert using a semiautomated software. Intraclass-correlation-coefficient (ICC) was used to study reproducibility. 3D-DL outperformed 2D-DL segmentation (Dice-scores: 0.94 ± 0.02 vs. 0.90 ± 0.09, P = 0.002) and was stable across vendors, field strengths and imaging views. The added value of combining DL with FT was revealed by higher correlations and lower Bland–Altman biases against RS for FT initialized by 3D-DL strains (r ≥ 0.91, |mean-bias|≤0.65%) than for full 3D-DL strains (r ≤ 0.80, |mean-bias|<3.07%). Semiautomated human vs. FT initialized by 3D-DL (ICC ≥ 0.76) and inter-human strain reproducibility was equivalent. Conclusion 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.
Related Topics
Concepts
Intraclass correlation
Segmentation
Initialization
Sørensen–Dice coefficient
Artificial intelligence
Reproducibility
Cardiac magnetic resonance
Strain (injury)
Feature tracking
Computer science
Nuclear medicine
Magnetic resonance imaging
Medicine
Mathematics
Pattern recognition (psychology)
Image segmentation
Anatomy
Radiology
Statistics
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/ehjimp/qyaf070
- https://academic.oup.com/ehjimp/advance-article-pdf/doi/10.1093/ehjimp/qyaf070/63421345/qyaf070.pdf
- OA Status
- hybrid
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410973183
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410973183Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/ehjimp/qyaf070Digital Object Identifier
- Title
-
Deep learning-based segmentation and strain analysis of left heart chambers from long-axis CMR imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-01Full publication date if available
- Authors
-
Jonas Leite, Émilie Bollache, Vincent Nguyen, Moussa Gueda Moussa, Thomas Wallet, Mikaël Prigent, Khaoula Bouazizi, Mohamed Zarai, Jérôme Lamy, Perrine Marsac, Niki Procopi, Michel Zeitouni, Alban Redheuil, Antonio Gallo, Elie Mousseaux, Gilles Montalescot, Thomas Dietenbeck, Nadjia KachenouraList of authors in order
- Landing page
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https://doi.org/10.1093/ehjimp/qyaf070Publisher landing page
- PDF URL
-
https://academic.oup.com/ehjimp/advance-article-pdf/doi/10.1093/ehjimp/qyaf070/63421345/qyaf070.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/ehjimp/advance-article-pdf/doi/10.1093/ehjimp/qyaf070/63421345/qyaf070.pdfDirect OA link when available
- Concepts
-
Intraclass correlation, Segmentation, Initialization, Sørensen–Dice coefficient, Artificial intelligence, Reproducibility, Cardiac magnetic resonance, Strain (injury), Feature tracking, Computer science, Nuclear medicine, Magnetic resonance imaging, Medicine, Mathematics, Pattern recognition (psychology), Image segmentation, Anatomy, Radiology, Statistics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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