Segmentation of Echocardiography Based on Deep Learning Model

Electronics

Hairui Wang¹  Jing Wu²  Nan Li²  Helin Huang²  Xiaomei Wu²  Cuizhen Pan³  Zhenyi Ge³  Chunqiang Hu³ 

[1]Academy for Engineering and Technology, Fudan University, Shanghai 200433, China

[2]Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China

[3]Department of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai 200032, China

关键词: UNet;    VGG16;    deep supervision;    image segmentation;    echocardiogram;   

DOI  :  10.3390/electronics11111714

来源: DOAJ

【 摘 要 】

In order to achieve the classification of mitral regurgitation, a deep learning network VDS-UNET was designed to automatically segment the critical regions of echocardiography with three sections of apical two-chamber, apical three-chamber, and apical four-chamber. First, an expert-labeled dataset of 153 echocardiographic videos and 2183 images from 49 subjects was constructed. Then, the convolution layer in the VGG16 network was used to replace the contraction path in the original UNet network to extract image features, and depth supervision was added to the expansion path to achieve the segmentation of LA, LV, and MV. The results showed that the Dice coefficients of LA, LV, and MV were 0.935, 0.915, and 0.757, respectively. The proposed deep learning network can achieve simultaneous and accurate segmentation of LA, LV, and MV in multi-section echocardiography, laying a foundation for quantitative measurement of clinical parameters related to mitral regurgitation.

【 授权许可】

Unknown