Abstract: 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.

Key words: abdominal artery segmentation, convolutional neural network, attention guided filtering, transfer learning