Application of Improved Convolutional Neural Network in Lung Image Segmentation - QIAN Baoxin, XIAO Zhiyong, SONG Wei (Jisuanji kexue yu tansuo, 2020)
CT imaging technology is an important means to assist doctors in diagnosing lung diseases. In order to solve the problem that the lung parenchyma is difficult to be segmented and extracted accurately from the CT images of the lung due to the complex structure of the lung tissues, an algorithm of lung segmentation with encoding/decoding mode is proposed. In order to obtain the multi-scale information of the image, the multi-scale image is firstly input into the network model, and the residual network structure is used as the coding module to expand the depth of the network without causing network degradation. In addition, the multi-scale information is fully extracted by using atrous spatial pyramid pooling (ASPP) between encoding and decoding. Finally, the cascade operation is used to cascade the captured information with the information of the coding layer, and the attention mechanism is combined to improve the segmentation accuracy. By testing 13465 CT images of 89 patients in the LUNA16 data set, the experimental accuracy reaches 99.56% and 99.33%, respectively, with the similarity coefficient and accuracy as the main evaluation criteria. Experimental results show that the method can effectively segment the lung parenchyma area, and the segmentation effect is better than other networks.