Ye-Zhan Zeng1, Yu-Qian Zhao2, Ping Tang1, Miao Liao3, Yi-Xiong Liang4, Sheng-Hui Liao4, Bei-Ji Zou4. 1. School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China. 2. School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China. Electronic address: zyq@csu.edu.cn. 3. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China. 4. School of Information Science and Engineering, Central South University, Changsha 410083, China.
Abstract
BACKGROUND AND OBJECTIVE: Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. METHODS: Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. RESULTS: The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. CONCLUSIONS: The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.
BACKGROUND AND OBJECTIVE: Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. METHODS: Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. RESULTS: The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. CONCLUSIONS: The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.
Authors: Marwan Abdellah; Nadir Román Guerrero; Samuel Lapere; Jay S Coggan; Daniel Keller; Benoit Coste; Snigdha Dagar; Jean-Denis Courcol; Henry Markram; Felix Schürmann Journal: Bioinformatics Date: 2020-07-01 Impact factor: 6.937