Squeeze-and-excitation Residual U-shaped Network for Left Myocardium Segmentation Based on Cine Cardiac Magnetic Resonance Images

Abstract

The left myocardium segmentation is significant for the diagnosis and prognosis of cardiovascular diseases. However, the internal part of the left myocardium is adjacent to the papillary muscle and trabeculae, and the external part is similar to the surrounding tissues in terms of grey level, which adds to difficulties facing segmentation. In this paper, the original datasets of cine cardiac magnetic resonance images were firstly pre-processed by extracting the region of interest. Then, squeeze-and-excitation residual U-shaped network (SERU-net), combining SE module and residual module, was built to segment the left myocardium. Finally, 75 pre-processed data were used to train SERU-net to predict the segmentation of 18 other cases. Compared with the ground truth, the average of Dice coefficient and Hausdorff distance are 0.902 and 2.697 mm. The correlation coefficient and mean deviation of end diastolic-left ventricular mass are 0.995 and 3.784 g, and that of end systolic-left ventricular mass are 0.993 and 2.338 g, respectively. The results show that SERU-net segmentation is close to the ground truth, and is prospective in assisting the diagnosis of heart diseases.

Keywords

• cine cardiac magnetic resonance image (cine-cmri)
• left myocardium segmentation
• squeeze-and-excitation residual u-shaped network
• deep learning