Evaluation of fully automated myocardial segmentation techniques in native and contrast‐enhanced T1‐mapping cardiovascular magnetic resonance images using fully convolutional neural networks
Notice bibliographique
Résumé
PURPOSE: T1-mapping cardiac magnetic resonance (CMR) imaging permits noninvasive quantification of myocardial fibrosis (MF); however, manual delineation of myocardial boundaries is time-consuming and introduces user-dependent variability for such measurements. In this study, we compare several automated pipelines for myocardial segmentation of the left ventricle (LV) in native and contrast-enhanced T1-maps using fully convolutional neural networks (CNNs). METHODS: Sixty patients with known MF across three distinct cardiomyopathy states (20 ischemic (ICM), 20 dilated (DCM), and 20 hypertrophic (HCM)) underwent a standard CMR imaging protocol inclusive of cinematic (CINE), late gadolinium enhancement (LGE), and pre/post-contrast T1 imaging. Native and contrast-enhanced T1-mapping was performed using a shortened modified Look-Locker imaging (shMOLLI) technique at the basal, mid-level, and/or apex of the LV. Myocardial segmentations in native and post-contrast T1-maps were performed using three state-of-the-art CNN-based methods: standard U-Net, densely connected neural networks (Dense Nets), and attention networks (Attention Nets) after dividing the dataset using fivefold cross validation. These direct segmentation techniques were compared to an alternative registration-based segmentation method, wherein spatially corresponding CINE images are segmented automatically using U-Net, and a nonrigid registration technique transforms and propagates CINE contours to the myocardial regions of T1-maps. The methodologies were validated in 125 native and 100 contrast-enhanced T1-maps using standard segmentation accuracy metrics. Pearson correlation coefficient r and Bland-Altman analysis were used to compare the computed global T1 values derived by manual, U-Net, and CINE registration methodologies. RESULTS: The U-Net-based method yielded optimal results in myocardial segmentation of native, contrast-enhanced, and CINE images compared to Dense Nets and Attention Nets. The direct U-Net-based method outperformed the CINE registration-based method in native T1-maps, yielding Dice similarity coefficient (DSC) of 82.7 ± 12% compared to 81.4 ± 6.9% (P < 0.0001). However, in contrast-enhanced T1-maps, the CINE-registration-based method outperformed direct U-Net segmentation, yielding DSC of 77.0 ± 9.6% vs 74.2 ± 18% across all patient groups (P = 0.0014) and specifically 73.2 ± 7.3% vs 65.5 ± 18% in the ICM patient group. High linear correlation of global T1 values was demonstrated in Pearson analysis of the U-Net-based technique and the CINE-registration technique in both native T1-maps (r = 0.93, P < 0.0001 and r = 0.87, P < 0.0001, respectively) and contrast-enhanced T1-maps (r = 0.93, P < 0.0001 and r = 0.98, P < 0.0001, respectively). CONCLUSIONS: The direct U-Net-based myocardial segmentation technique provided accurate, fully automated segmentations in native and contrast-enhanced T1-maps. Myocardial borders can alternatively be segmented from spatially matched CINE images and applied to T1-maps via deformation and propagation through a modality-independent neighborhood descriptor (MIND). The direct U-Net approach is more efficient in myocardial segmentation of native T1-maps and eliminates cross-technique dependence. However, the CINE-registration-based technique may be more appropriate for contrast-enhanced T1-maps and/or for patients with dense regions of replacement fibrosis, such as those with ICM.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».