Left ventricular myocardium segmentation in coronary computed tomography angiography using 3D deep attention u-net

Cardiovascular diseases (CVD) are the leading cause of disability and death worldwide. Many parameters based on left ventricular myocardium (LVM), including left ventricular mass, the left ventricular volume, and the ejection fraction (EF) are widely used for disease diagnosis and prognosis prediction. To investigate the relationship between parameters derived from the LVM and various heart diseases, it is crucial to segment the LVM in a fast and reproducible way. However, different diseases can affect the structure of the LVM, which increases the complexity of the already time-consuming manual segmentation work. In this work, we propose to use a 3D deep attention U-Net method to segment the LVM contour for cardiac CT images automatically. We used 50 patients’ cardiac CT images to test the proposed method. The Dice similarity coefficient (DSC), sensitivity, specificity, and mean surface distance (MSD) were 87% ± 5%, 87% ± 4%, 92% ± 3% and 0.68 ± 0.15 mm, which demonstrated the detection and segmentation accuracy of the proposed method.