3D Densely Connected Convolution Neural Networks for Pulmonary Parenchyma Segmentation from CT Images

Abstract

Lung cancer is one of the deadliest diseases in the world today. It kills many people every year. For the diagnosis and treatment of lung cancer, accurate segmentation of lung tissue from CT images is an important process. It is necessary to design a fast and accurate segmentation method to accomplish this task. In the traditional computer-Aided diagnosis system, the segmentation of Lung parenchyma is very complex, and the segmentation result depends on the performance of the parameters set in the previous stage. In order to solve these problems, we propose a 3D densely connected convolution neural network which based on deep learning. It has three densely connected blocks and three deconvolution layers. The experimental data set was taken from the public LIDC-IDRI database. A total of 888 samples with slice thickness less than 2.5 mm were selected in the experiments. And the number of samples of training set, test set and validation set is 708, 90 and 90 respectively. In addition, the experimental results show that our method is more accurate than 3D-Unet, but it requires less training parameters.