Model-based cardiac CT segmentation and 3D heart reconstruction

Liver cancer is a serious disease in human beings. An effective way to cure liver cancer is the liver transplant operation. However, to make the surgical plan, the doctors need to know the structure, location and thickness of the hepatic vein. Therefore, hepatic vein segmentation is an initial and crucial step in liver cancer surgery. This thesis focuses on segmentation of hepatic veins from abdominal CT images. The purpose of this work is to obtain a volumetric hepatic vein model from the abdominal CT for liver transplant operation. To solve this problem, this thesis proposes a fast marching method driven by Gaussian mixture models (GMM) to segment hepatic vein from CT images. Anisotropic smoothing is applied to the original CT data to remove the noise. After that, GMMs are built for both hepatic vein area and non-hepatic vein areas based on hand-draw sampling points. The fast-marching propagation speed at each location is controlled by the generated GMMs. After that, a parametric cylinder model based algorithm is proposed to remove the unnecessary vena cava from the segmentation result. The segmentation results are analyzed and discussed.

Author dc:creator

• SONG ZHIYUAN

• Hepatic Vein Segmentation, Fars-marching method, gaussian mixture model