| Summary: | Scar quantification on cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) images is important in risk stratifying patients with hypertrophic cardiomyopathy (HCM) due to the importance of scar burden in predicting clinical outcomes. We aimed to develop a machine learning (ML) model that contours left ventricular (LV) endo- and epicardial borders and quantifies CMR LGE images from HCM patients.We retrospectively studied 2557 unprocessed images from 307 HCM patients followed at the University Health Network (Canada) and Tufts Medical Center (USA). LGE images were manually segmented by two experts using two different software packages. Using 6SD LGE intensity cutoff as the gold standard, a 2-dimensional convolutional neural network (CNN) was trained on 80% and tested on the remaining 20% of the data. Model performance was evaluated using the Dice Similarity Coefficient (DSC), Bland-Altman, and Pearson’s correlation. The 6SD model DSC scores were good to excellent at 0.91 ± 0.04, 0.83 ± 0.03, and 0.64 ± 0.09 for the LV endocardium, epicardium, and scar segmentation, respectively. The bias and limits of agreement for the percentage of LGE to LV mass were low (-0.53 ± 2.71%), and correlation high (r = 0.92). This fully automated interpretable ML algorithm allows rapid and accurate scar quantification from CMR LGE images. This program does not require manual image pre-processing, and was trained with multiple experts and software, increasing its generalizability. Author summary Accurate scar quantification of cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) images is important in managing hypertrophic cardiomyopathy (HCM) patients. We developed a 2D convolutional neural network to quantify CMR LGE in HCM patients that is computationally interpretable and trained using multicenter data analyzed by 2 expert readers using 2 different analysis packages. Our model demonstrated low bias and limits of agreement and high correlation with expert analysis. Benchmarking comparison was performed between our algorithm and standard U-Net model with and without cropped raw images. Our method showed superior performance and has high potential for clinical adaptability.
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