Improved U-NET network for pulmonary nodules segmentation

Abstract

Since pulmonary nodules in CT images are very small and easily confusing with other tissues, there are still many problems in the pulmonary nodule segmentation. This paper presents an improved lung nodule segmentation algorithm based on U-NET network. Firstly, CT images are transformed and normalized, and the lung parenchyma is obtained by simple and efficient morphological method. Then, the U-NET network is improved, which mainly includes the dataset rebuilding, convolutional layer, pooling layer and upsampled layer. And we introduced residual network, which has improved the network training effect. Besides, we designed batch standardization operation, which has speeded up the network training and improves the network stability. Finally, we used the new dataset to train and test the improved U-NET network. A large number of experiments show that the proposed method can effectively improve the segmentation accuracy of pulmonary nodules. It is a great work with theoretical and practical value.