Improving a Deep Learning Model to Accurately Diagnose LVNC

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.

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach

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.

A highly accurate method for quantifying LVNC cardiomyophaty

Left ventricular non-compaction (LVNC) is defined by an increase of trabeculations in left ventricular endo-myocardium. Although LVNC can be in isolation, an increase in hypertrabeculation often accompanies genetic cardiomyopthies. Several enhancements are proposed and implemented to improve a software tool for the automatic quantification of the exact hyper-trabeculation degree in the left ventricular myocardium for a population of patients with LVNC cardiomyopathy (QLVTHC-NC). The software tool is developed and evaluated for a population of 18 patients (133 cardiac images). An end-diastolic cardiac magnetic resonance images of the patients are the input of the software, whereas the left ventricular mass, volumes and proportion of trabeculation produced by the compacted zone and the trabeculated zone are the outputs. Significant improvements are obtained with respect to the manual process, so saving valuable diagnosis time. Comparing the method proposed with the fractal proposal to differentiate LVNC and non-LVNC patients in subjects with previously diagnosed LVNC cardiomyophaty, QLVTHC-NC presents higher diagnostic accuracy and lower complexity and cost than the fractal criterio.