In liver surgical planning

accurate and effective vessel segmentation is the basis of necessary topological analysis for preoperative volumetric assessment and surgery simulation. An algorithm is proposed for automatic hepatic vessel segmentation in multi-phase contrast-enhanced CT. First

segmented CT images are processed with anisotropy filter for noise reduction. With the segmented mask

the influence of related organs

such as ribs

is avoided

and the computational expense is reduced. An improved Hessian-based enhancing filter with intensity information is designed to overcome the discontinuity of the junction region. Intensity information is used for noise reduction rather than Frobenius matrix norm. Finally

an adaptive multi-scale region growing method is implemented for vessel segmentation in the enhanced result. The mean values of current segmented target and background are used for the adaptive threshold selection. A multi-scale iteration is implemented in the growing region to avoid intensity inhomogeneity. Five sets of clinical multi-phase contrast-enhanced CT images were used for the evaluation. In Cases1-Case4

images from portal phase were chosen as input

and only portal vessel systems were segmented. Topological analysis shows that fifth-stage bifurcation or sixth-stage bifurcation could be detected with good accuracy. In Case5

images from the delayed phase were chosen as input

and both portal vessel and hepatic vessel systems were extracted. Topological analysis of a single hepatic vein demonstrated that fifth-stage bifurcation could still be detected. Experimental results indicated that the trunks and branches of hepatic vessels were completely segmented. This paper presented a novel automatic segmentation for hepatic vessels. Different parameters were assigned for the region growing on different multi-scale enhanced images. The results showed the algorithm was effective and accurate. In addition

it provided the corrected topological structures of hepatic vessels.