Echocardiographic segmentation based on semi-supervised deep learning with attention mechanism

Liang，Jiajun1; Pan，Huijuan2; Xiang，Zhuo1; Qin，Jing3; Qiu，Yali1; Guo，Libao1; Wang，Tianfu1; Jiang，Wei4; Lei，Baiying1

2023

10.1007/s11042-023-16044-y

Multimedia Tools and Applications   Impact Factor & Quartile

1380-7501

1573-7721

Echocardiographic examination is one of the main methods for clinical diagnosis, management and follow-up of heart diseases. Echocardiographic segmentation is an essential step for obtaining precise measurements and accurate diagnosis. However, the current methods are mostly time-consuming, relatively subjective, and produce inconsistent results due to varying ultrasound image quality. To solve these problems, we propose an automatic 2D echocardiographic segmentation method, which is objective and robust for the change of image quality. Specifically, our method first constructs an echocardiographic motion estimation network to extract the heart motion features for the echocardiographic segmentation network. Then, based on semi-supervised learning, the echocardiographic segmentation network is trained by labeled images’ ground truth and unlabeled images’ pseudo labels, which are derived from the motion features. In addition, we introduce attention mechanism to observe its impact on segmentation performance. Experimental results show that the peak signal-to-noise ratio and the structural similarity index between the target images and the images reconstructed by the motion features are over 30dB and 92%, respectively. The echocardiographic segmentation network achieves 95.93% accuracy and 90.94% dice similarity coefficient in the segmentation of cardiac end-diastolic, and achieves 96.06% accuracy and 91.51% dice similarity coefficient in the segmentation of cardiac end-systolic. These results prove that the motion features and segmentation results obtained from our method are effective and accurate. Our code is publicly available at: https://github.com/cherish-fere/motion_net.

2D echocardiography Attention mechanism Medical image segmentation Semi-supervised learning

SCI

English

Others

WOS:001043714700001

COMPUTER SCIENCE

2-s2.0-85166958619

Scopus

1.School of Biomedical Engineering,Health Science Center,National-Regional Key Technology Engineering Laboratory for Medical Ultrasound,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging,Shenzhen University,Shenzhen,Guangdong,518060,China
2.Department of Urology,Southern University of Science and Technology Hospital,Shenzhen,China
3.Centre for Smart Health,School of Nursing,The Hong Kong Polytechnic University,Hong Kong
4.Department of Ultrasound,Huazhong University of Science and Technology Union Shenzhen Hospital,Shenzhen,China

Liang，Jiajun,Pan，Huijuan,Xiang，Zhuo,et al. Echocardiographic segmentation based on semi-supervised deep learning with attention mechanism[J]. Multimedia Tools and Applications,2023.

Liang，Jiajun.,Pan，Huijuan.,Xiang，Zhuo.,Qin，Jing.,Qiu，Yali.,...&Lei，Baiying.(2023).Echocardiographic segmentation based on semi-supervised deep learning with attention mechanism.Multimedia Tools and Applications.

Liang，Jiajun,et al."Echocardiographic segmentation based on semi-supervised deep learning with attention mechanism".Multimedia Tools and Applications (2023).