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Abstract #2224

Left Ventricle Segmentation with Densely Connected Full Convolutional Network

Zhanli Hu1, Yin Wu1, Siyue Li1, Wenjian Qin1, Dong Liang1, Xin Liu1, Yongfeng Yang1, and Hairong Zheng1

1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Cardiac functional analysis is important in heart disease diagnosis. Conventional manual segmentation of left ventricle is time consuming and observer dependent. Our proposed Densely Connected Full Convolutional Network (DenseV-Net) enables automatically process medical images. Its densely connected convolutional block consists of residual calculation with Elu used as active function. The results show that the proposed DenseV-Net can efficiently segment left ventricle from cardiac cines with mean DSC of 0.90±0.12, more accurate compared to V-Net (0.85±0.13, P<0.05). The method offers a feasible way for efficient analysis of cardiac function.

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