Chan Wu1, Tianyu Fu1, Yuanjin Gao2, Yuhan Liu1, Jingfan Fan1, Danni Ai1, Hong Song3, Jian Yang1. 1. Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China. 2. Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing, 100853, China. 3. School of Software, Beijing Institute of Technology, Beijing, 100081, China.
Abstract
PURPOSE: The liver segments divided by Couinaud classification method are used to understand the functional anatomy of liver, which is significant in hepatic resection surgery. In Couinaud classification method, each third-order branch of the portal vein (PV) defines the supplied territory of a corresponding liver segment. However, the accuracies of the reconstruction and classification of PV are affected by the complicated structure of the vein. The purpose of this paper is to develop a separation and classification method that can accurately extract the liver segments. METHODS: In this paper, a multiple feature-based method is proposed to obtain liver segments. Because the portal and hepatic veins usually connect in the vessel segmentation result, the PV is first completely separated based on the different strategies for minimal node cut using fused features of topology and appearance. Meanwhile, all bifurcation nodes of PV are detected. The bifurcation nodes are initial ordered through their linkages to classify the branches. Then, the feature of the vascular topology is used to refine the orders of bifurcation nodes. The bifurcation nodes with the refined orders classify the branches between them, and the third-order branches of PV are obtained. The liver segments are eventually obtained through the third-order branches. RESULTS: The separation and classification in the proposed method are evaluated on the CT and MR datasets. The average values of Dice, Jaccard, Recall, and Precision obtained by the proposed method are 93.00%, 87.90%, 93.47%, and 93.19%, respectively. Compared with the state-of-the-art methods, the separation results obtained by the proposed method are more accurate. The branches of PV are classified based on the separation result. According to the third-order branches, eight liver segments correspond to the different functional areas are precisely extracted. CONCLUSIONS: The proposed method achieves a high accuracy for the liver segment extraction. And the extracted liver segments are significant for the preplanning of resection surgery.
PURPOSE: The liver segments divided by Couinaud classification method are used to understand the functional anatomy of liver, which is significant in hepatic resection surgery. In Couinaud classification method, each third-order branch of the portal vein (PV) defines the supplied territory of a corresponding liver segment. However, the accuracies of the reconstruction and classification of PV are affected by the complicated structure of the vein. The purpose of this paper is to develop a separation and classification method that can accurately extract the liver segments. METHODS: In this paper, a multiple feature-based method is proposed to obtain liver segments. Because the portal and hepatic veins usually connect in the vessel segmentation result, the PV is first completely separated based on the different strategies for minimal node cut using fused features of topology and appearance. Meanwhile, all bifurcation nodes of PV are detected. The bifurcation nodes are initial ordered through their linkages to classify the branches. Then, the feature of the vascular topology is used to refine the orders of bifurcation nodes. The bifurcation nodes with the refined orders classify the branches between them, and the third-order branches of PV are obtained. The liver segments are eventually obtained through the third-order branches. RESULTS: The separation and classification in the proposed method are evaluated on the CT and MR datasets. The average values of Dice, Jaccard, Recall, and Precision obtained by the proposed method are 93.00%, 87.90%, 93.47%, and 93.19%, respectively. Compared with the state-of-the-art methods, the separation results obtained by the proposed method are more accurate. The branches of PV are classified based on the separation result. According to the third-order branches, eight liver segments correspond to the different functional areas are precisely extracted. CONCLUSIONS: The proposed method achieves a high accuracy for the liver segment extraction. And the extracted liver segments are significant for the preplanning of resection surgery.