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

doi:10.11938/cjmr20212900

Abstract

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.

Key words: cine cardiac magnetic resonance image (cine-CMRI), left myocardium segmentation, squeeze-and-excitation residual U-shaped network, deep learning

CLC Number:

O482.53

WANG Hui, WANG Tiantian, WANG Lijia. Squeeze-and-excitation Residual U-shaped Network for Left Myocardium Segmentation Based on Cine Cardiac Magnetic Resonance Images[J]. Chinese Journal of Magnetic Resonance, 2023, 40(4): 435-447.

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	左心肌		LV
	DM	HD/mm	DM	HD/mm
FCN^[23]	0.878 (0.031)	3.086 (1.129)	0.896 (0.056)	2.920 (0.899)
U-net^[15]	0.903 (0.021)	2.736 (0.841)	0.916 (0.050)	2.825 (1.103)
U-net++^[16]	0.894 (0.027)	2.837 (0.617)	0.922 (0.026)	2.588 (0.715)
SegNet^[14]	0.877 (0.034)	3.039 (0.871)	0.909 (0.024)	2.974 (0.622)
PSPNet^[13]	0.887 (0.025)	2.846 (0.500)	0.897 (0.039)	2.846 (0.923)
SERU-net（本文方法）	0.902 (0.019)	2.697 (0.582)	0.928 (0.029)	2.477 (0.796)

Method	ED_LVM_R	ED_LVM_MD/g	ES_LVM_R	ES_LVM_MD/g
FCN	0.992	5.793	0.990	3.854
U-net	0.993	4.946	0.990	3.337
U-net++	0.993	4.272	0.991	2.703
SegNet	0.994	4.165	0.992	2.857
PSPNet	0.992	4.364	0.991	2.794
SERU-net（本文方法）	0.995	3.784	0.993	2.338

方法	EDV_R	EDV_MD/mL	ESV_R	ESV_MD/mL	EF_R	EF_MD
FCN	0.991	5.728	0.973	4.407	0.980	-0.023
U-net	0.995	8.333	0.980	6.897	0.995	-0.026
U-net++	0.995	7.390	0.978	4.428	0.969	-0.021
SegNet	0.993	9.360	0.991	5.644	0.991	-0.023
PSPNet	0.990	6.780	0.986	5.449	0.977	-0.030
SERU-net（本文方法）	0.994	5.669	0.986	2.389	0.983	-0.008