腹部动脉血管分割对于胃癌淋巴结的转移和肝动脉变异类型的判断至关重要。针对腹部动脉血管分割精度低、易断裂等问题，提出一种改进卷积网络架构的腹部动脉分割方法。卷积网络的编码部分使用带有卷积注意的预训练模块(resnet34)，避免了梯度消失且可更好地获取图像的特征信息。为了扩大感受野和聚集多尺度特征信息，提出了一种新的多尺度特征融合模块。此外，动脉血管的边缘结构信息的学习至关重要，引入注意力导向滤波作为信息扩展路径，使输出特征更加结构化，提升血管分割的精度。所提方法在腹部动脉血管分割的实验结果表明，与基础网络U-Net相比，所提方法在灵敏度和交并比上分别提升了2.84%和1.19%。与CE-Net网络相比，在灵敏度和交并比上分别提升了1.34%和161%。


Abdominal artery segmentation is an essential task for the diagnosis of gastric cancer lymph node metastasis and the judgment of hepatic artery variant type. In order to solve the problems of low segmentation accuracy and easy fracture of abdominal artery, this paper proposes an abdominal artery segmentation method based on improved convolutional neural network. A pre-training module (resnet34) with convolutional attention is employed in encoding part of convolutional network to avoid the disappearance of gradients and better obtain the feature information of the images. In order to expand the receptive field and gather multi-scale feature information, a new multi-scale feature fusion module is proposed. In addition, the learning of the edge structure information of arteries is very significant. Attention guide filtering is introduced as the information expansion path to make the output features more structured and improve the accuracy of vascular segmentation. The proposed method is used to evaluate the performance of the abdominal artery segmentation. The experimental results show that, compared with the basic network U-Net, the sensitivity and intersection-over-ｕｎｉｏｎ (IOU) of the proposed method are increased by 2.84% and 1.19%, respectively. Compared with the network CE-Net, the sensitivity and IOU are improved by 1.34% and 1.61%, respectively.


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