Multiple feature‐based portal vein classification for liver segment extraction

Abstract

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.

Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network

“…It is a major challenge because it imposes to have actual segment specimens. Several recent studies have adopted new approaches, including quantitative assessment, qualitative assessment, and indirect assessment [ 4 , 5 , 8 , 9 ]. However, they all have a small sample size problem.…”

“…In the past two decades, a lot of research work has been done in computer-assisted liver segment segmentation [ 4 – 21 ], most of them using traditional machine learning techniques [ 4 , 7 – 21 ], which cannot meet the needs of clinical applications in terms of segmentation performance and efficiency. The method proposed by Lebre et al [ 8 ] requires first segmentation of the hepatic vessels using the skeletonization process, and then the main direction of the largest vessels was extracted to achieve separation of different liver segments.…”

“…However, the whole process took more than 8 min, and good results are highly dependent on accurate vascular segmentation, which is difficult owing to the complex intertwined vascular anatomy in the liver. To tackle this problem, in a recently published paper, Wu et al [ 4 ] introduced a multiple feature-based method. Their method improved the accuracy of vessel separation and was subsequently expected to improve the segmentation of liver segments.…”