Search

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 (LVNC) is defined by an increase of trabeculations in left ventricular (LV) endomyocardium. Although LVNC can be in isolation, an increase in hypertrabeculation often accompanies genetic cardiomyopathies. Current methods for quantification of LV trabeculae have limitations. Several improvements are proposed and implemented to enhance a software tool to quantify the trabeculae degree in the LV myocardium in an accurate and automatic way for a population of patients with genetic cardiomyopathies (QLVTHCI). The software tool is developed and evaluated for a population of 59 patients (470 end-diastole cardiac magnetic resonance images). This tool produces volumes of the compact sector and the trabecular area, the proportion between these volumes, and the left ventricular and trabeculated masses. Substantial enhancements are obtained over the manual process performed by cardiologists, so saving important diagnosis time. The parallelization of the detection of the external layer is proposed to ensure real-time processing of a patient, obtaining speed-ups from 7.5 to 1500 with regard to QLVTHCI and the manual process used traditionally by cardiologists. Comparing the method proposed with the fractal proposal to differentiate LVNC and non-LVNC patients among 27 subjects with previously diagnosed cardiomyopathies, QLVTHCI presents a full diagnostic accuracy, while the fractal criteria achieve 78%. Moreover, QLTVHCI can be installed and integrated in hospitals on request, whereas the high cost of the license of the fractal method per year of this tool has prevented reproducibility by other medical centers.