Abstract：Automatic segmentation of hepatic vein and portal vein based on preoperative CT images has important clinical value for liver segment. However, in the venous CT image of the liver, little difference is in the density of the hepatic vein and portal vein, and the vascular structure is also complicated, so there has been a challenge with automatic extraction of the hepatic vein and portal vein in 3D. To solve this problem, this paper proposed a network architecture named W-Net based on convolutional neural network (CNN). The network architecture made a full use of the difference in the 3D structure for the hepatic vein and portal vein. We also set a loss function for the extraction of all vessels and portal vein. Then the model would optimize the weighted sum of the two loss functions to automatically learn the features of all vessels and portal vein to achieve the best extraction result. Hepatic vein was obtained by subtracting the two result. In this paper, we used 10 sets of venous abdominal CT images from the public data set 3Dircadb01 for network model construction, and 10 groups for testing. We mainly used the Dice coefficient as the evaluation standard. Finally, in the liver area, the Dice coefficient and accuracy of blood vessels reached 0.715 and 0.970, the Dicecoefficient and accuracy of hepatic vein reached 0.597 and 0.984, and the Dice and accuracy of portal vein reached 0.608 and 0.970. We also tested in 10 sets of clinical data, and the method could effectively separate the hepatic vein and portal vein. The experimental results showed that the proposed method possessed better feature extraction ability and generalization ability, and had good performance in public data and clinical data.
孙锦峰, 丁辉, 王广志. 基于W-Net的肝静脉和肝门静脉全自动分割[J]. 中国生物医学工程学报, 2019, 38(5): 513-521.
Sun Jinfeng, Ding Hui, Wang Guangzhi. Automatic Segmentation of Hepatic Vein and Portal Vein Based on W-Net. Chinese Journal of Biomedical Engineering, 2019, 38(5): 513-521.
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