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Hyper-trabeculation or non-compaction in the left ventricle of the myocardium (LVNC) is a recently classified form of cardiomyopathy. Several methods have been proposed to quantify the trabeculae accurately in the left ventricle, but there is no general agreement in the medical community to use a particular approach. In the previous work, we proposed DL-LVTQ, a deep-learning approach for left ventricular trabecular quantification based on a U-Net CNN architecture. In this work, we have extended and adapted DL-LVTQ to cope with patients with different particularities and cardiomyopathies. Patient images were taken from different scanners and hospitals. We have modified and adapted the U-Net convolutional neural network to account for the different particularities of a heterogeneous group of patients with multiple cardiomyopathies and inherited cardiomyopathies. The inclusion of new groups of patients has increased the accuracy, specificity and Kappa values while maintaining the sensitivity of the proposed method. Therefore, a better-prepared diagnosis tool is ready for various cardiomyopathies with different characteristics. Cardiologists have considered that 98.9% of the evaluated outputs are verified clinically for diagnosis. Therefore, the high precision to segment the different cardiac structures allows us to make a robust diagnostic system objective and faster, decreasing human error and time spent. [ABSTRACT FROM AUTHOR]

Accurate diagnosis of Left Ventricular Noncompaction Cardiomyopathy (LVNC) is critical for proper patient treatment but remains challenging. This work improves LVNC detection by improving left ventricle segmentation in cardiac MR images. Trabeculated left ventricle indicates LVNC, but automatic segmentation is difficult. We present techniques to improve segmentation and evaluate their impact on LVNC diagnosis. Three main methods are introduced: (1) using full 800 × 800 MR images rather than 512 × 512; (2) a clustering algorithm to eliminate neural network hallucinations; (3) advanced network architectures including Attention U-Net, MSA-UNet, and U-Net++.Experiments utilize cardiac MR datasets from three different hospitals. U-Net++ achieves the best segmentation performance using 800 × 800 images, and it improves the mean segmentation Dice score by 0.02 over the baseline U-Net, the clustering algorithm improves the mean Dice score by 0.06 on the images it affected, and the U-Net++ provides an additional 0.02 mean Dice score over the baseline U-Net. For LVNC diagnosis, U-Net++ achieves 0.896 accuracy, 0.907 precision, and 0.912 F1-score outperforming the baseline U-Net. Proposed techniques enhance LVNC detection, but differences between hospitals reveal problems in improving generalization. This work provides validated methods for precise LVNC diagnosis.

Background and Objective: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the trabecular volume to the total volume of the external wall of the left ventricle (VT%) has been proposed to diagnose this illness., Methods: This paper presents the use of a deep learning-based method to measure the (VT%) value and diagnose this rare cardiomyopathy. The population used in this research was composed of 277 patients suffering from hypertrophic cardiomyopathy. 134 patients only suffered hypertrophic cardiomyopathy, and 143 also suffered left ventricular non-compaction. Our deep learning solution is based on a 2D U-Net. This artificial neural network (ANN) was trained on short-axis magnetic resonance imaging to segment the left ventricle's internal cavity, external wall, and trabecular tissue. 5-fold cross-validation was performed to ensure the robustness of the results. The Dice coefficient of the three classes was computed as a measure of the precision of the segmentation. Based on this segmentation, the percentage of the trabecular volume (VT%) was computed. Two specialist cardiologists rated the segmentation produced by the neural network for 25 patients to evaluate the clinical validity of the outputs. The computed VT% was used to automatically diagnose the 277 patients depending on whether or not a given threshold was exceeded. A receiver operating characteristic analysis was also performed., Results: According to the cross-validation results, the average and standard deviation of the Dice coefficient for the internal cavity, external wall, and trabeculae were 0.96±0.00, 0.89±0.00, and 0.84±0.00, respectively. The cardiologists rated 99.5% of the evaluated segmentations as clinically valid for diagnosis, outperforming existing automatic traditional tools. The area under the ROC curve was 0.94 (95% confidence interval, 0.91-0.96). The accuracy, sensitivity, and specificity values of diagnosis using a threshold of 25% were 0.87, 0.93, and 0.80, respectively., Conclusions: The U-Net neural network can achieve excellent results in the delineation of different cardiac structures of short-axis cardiac MRI. The high-quality segmentation allows for the correct measurement of left ventricular hyper-trabeculation and a definitive diagnosis of LVNC illness. Using this kind of solution could lead to more objective and faster analysis, reducing human error and time spent by cardiologists., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)