An Res-Unet Method for Pulmonary Artery Segmentation of CT Images

In the recognition of pulmonary embolism, the accuracy of pulmonary artery segmentation plays a key role. Due to the irregular shape of pulmonary artery and the complex adjacent tissues, it is very challenging to segment pulmonary artery using traditional convolutional neural network. Therefore, an improved Res-Unet method for pulmonary artery segmentation is proposed in this paper. To begin with, the U-net structure is used as the basis structure to allow efficient information flow. Secondly, in order to improve the gradient circulation of the network, our model introduces residual connections based on the U-net structure, that is, adding a connection from the input to the output of the two convolutions and performing a convolution operation. Finally, to quick converge, we use a hybrid loss function, which is linearly combined by Dice loss and Cross Entropy loss. The experimental results show that the proposed framework ranks higher than U-net on recall, precision and Dice, yielding results comparable to that of manual segmentation.