AUTOMATIC SEGMENTATION ALGORITHM OF LUNG PARENCHYMA IN CT IMAGES BASED ON IMPROVED UNET NETWORK
Segmentation of lung parenchyma is an important step in computer-aided diagnosis of lung cancer.Aimed at the problems of insufficient segmentation accuracy and slow convergence speed of Unet,a lung parenchymal segmentation algorithm based on improved Unet is proposed.K-means clustering and convex hull scanning algorithm were used for pre-segmentation to complete the positioning and correction of lung parenchyma.Based on the Unet structure,the Sobel convolutional layer was introduced to strengthen the high-pass filtering of the edge area,and the random inactivation module was added to the feature fusion to further improve the segmentation accuracy.Combining traditional image processing methods with deep learning,an optimized and improved segmentation model was obtained.Experiments show that the algorithm can segment lung parenchyma accurately and efficiently,with an average Dice similarity coefficient of 0.983 4,and the convergence speed and segmentation performance are better than other new segmentation algorithms.
国家科技期刊平台
NSTL
万方数据
维普
