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ABSTRACT: In living donor liver transplantation, the volume of the potential graft must be measured to ensure sufficient liver function after surgery. Couinaud divided the liver into 8 functionally independent segments. However, this method is not simple to perform in 3D space directly. Thus, we propose a rapid method to segment the liver based on the hepatic vessel tree. The most important step of this method is vascular projection. By carefully selecting a projection plane, a 3D point can be fixed in the projection plane. This greatly helps in rapid classification. This method was validated by applying it to a 3D liver depicted on CT images, and the result was in good agreement with Couinaud's classification.
Computer Methods and Programs in Biomedicine 12/2008; 92(3):274-8. · 1.52 Impact Factor
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ABSTRACT: Generating tetrahedral meshes from CT slices is a very important step in a hepatic surgical simulation. Manual creating of these tetrahedrons is often very hard or impossible, because of hepatic geometry complexity. Therefore we have developed a common framework for generating liver tetrahedral FEM models from CT slices. This framework is consists of several steps includes liver segmentation, 3D reconstruction, mesh simplification and Delaunay triangulation. The tetrahedrons we finally get can be used for further FEM analysis. This framework could be extended to use in the other human tissue modeling with small change.
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on; 06/2008
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ABSTRACT: For establishing a plan of Living Donor Liver Transplantation (LDLT), it is very important to estimate the volume of each
liver segment. Usually Couinaud’s classification is used to segment a liver, which is based on the liver anatomy. However,
it is not easy to perform this method in a 3D space directly. In this paper, a fast segment method based on the hepatic vessel
tree was proposed. This method was composed of four main steps: vasculature segmentation, 3D thinning, vascular tree pruning
and classification, and vascular projection and curve fitting. This method was validated by application to a 3D liver from
CT data, and it was shown to approximate closely Couinaud’s classification with high speed.
06/2008: pages 270-276;
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Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on; 08/2007
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Medical Imaging and Informatics, 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007, Revised Selected Papers; 01/2007
