Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases.The accuracy of segmentation is suffering from the complex vascular structure.In ...Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases.The accuracy of segmentation is suffering from the complex vascular structure.In this paper,an Improved Residual Attention U-Net(IRAU-Net)aiming to segment pulmonary vessel in 3D is proposed.To extract more vessel structure information,the Squeeze and Excitation(SE)block is embedded in the down sampling stage.And in the up sampling stage,the global attention module(GAM)is used to capture target features in both high and low levels.These two stages are connected by Atrous Spatial Pyramid Pooling(ASPP)which can sample in various receptive fields with a low computational cost.By the evaluation experiment,the better performance of IRAU-Net on the segmentation of terminal vessel is indicated.It is expected to provide robust support for clinical diagnosis and treatment.展开更多
To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scann...To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.展开更多
文摘Automatic segmentation of pulmonary vessels is a fundamental and essential task for the diagnosis of various pulmonary vessels diseases.The accuracy of segmentation is suffering from the complex vascular structure.In this paper,an Improved Residual Attention U-Net(IRAU-Net)aiming to segment pulmonary vessel in 3D is proposed.To extract more vessel structure information,the Squeeze and Excitation(SE)block is embedded in the down sampling stage.And in the up sampling stage,the global attention module(GAM)is used to capture target features in both high and low levels.These two stages are connected by Atrous Spatial Pyramid Pooling(ASPP)which can sample in various receptive fields with a low computational cost.By the evaluation experiment,the better performance of IRAU-Net on the segmentation of terminal vessel is indicated.It is expected to provide robust support for clinical diagnosis and treatment.
文摘To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.
