Jani VP, Ostovaneh M, Chamera E, Kato Y, Lima JAC, Ambale-Venkatesh B. Deep Learning for Automatic Volumetric Segmentation of Left Ventricular Myocardium and Ischemic Scar from Multi-Slice LGE-CMR.
Eur Heart J Cardiovasc Imaging 2024:jeae022. [PMID:
38244222 DOI:
10.1093/ehjci/jeae022]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/09/2023] [Accepted: 01/18/2024] [Indexed: 01/22/2024] Open
Abstract
PURPOSE
This study details application of deep learning for automatic volumetric segmentation of left ventricular myocardium and scar and automated quantification of myocardial ischemic scar burden from late-gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR).
MATERIALS AND METHODS
We included 501 images and manual segmentations of short-axis LGE-CMR from over 20 multinational sites, from which 377 studies were used for training and 124 studies from unique participants for internal validation. A third test set of 52 images was used for external evaluation. Three models, U-Net, Cascaded U-Net, and U-Net++, were trained with a novel adaptive weighted categorical cross entropy loss function. Model performance was evaluated using concordance correlation coefficients (CCC) for left ventricular (LV) mass and percent myocardial scar burden.
RESULTS
Cascaded U-Net was found to be the best model for quantification of LV mass and scar percentage. The model exhibited a mean difference of -5 ± 23 g for LV mass, -0.4 ± 11.2 g for scar mass, and -0.8 ± 7% for percent scar. CCC were 0.87, 0.77, and 0.78 for LV mass, scar mass, and percent scar burden, respectively, in the internal validation set and 0.75, 0.71, and 0.69, respectively, in the external test set. For segmental scar mass, CCC was 0.74 for apical scar, 0.91 for midventricular scar, and 0.73 for basal scar, demonstrating moderate to strong agreement.
CONCLUSION
We successfully trained a convolutional neural network for volumetric segmentation and analysis of left ventricular scar burden from LGE-CMR images in a large, multinational cohort of participants with ischemic scar.
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