Comparison of three U-Net family architectures for left ventricular myocardial wall automatic segmentation

Left ventricular (LV) segmentation is an important process which can provide quantitative clinical measurements such as volume, wall thickness and ejection fraction. The development of an automatic LV segmentation procedure is a challenging and complicated task mainly due to the variation of the heart shape from patient to patient, especially for those with pathological and physiological changes. In this study, we focus on the implementation, evaluation and comparison of three different Deep Learning architectures of the U-Net family: the custom 2-D U-Net, the ResU-Net++ and the DenseU-Net, in order to segment the LV myocardial wall. Our approach was applied to cardiac CT datasets specifically derived from patients with hypertrophic cardiomyopathy. The results of the models demonstrated high performance in the segmentation process with minor losses. The model revealed a dice score for U-Net, Res-U-net++ and Dense U-Net, 0.81, 0.82 and 0.84, respectively.