Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning

Abstract

The structure and morphology of the hepatic vessels and their relationship between tumors and liver segments are major interests to surgeons for liver surgical planning. In case of living donor liver transplantation (LDLT), the most important step in determining donor suitability is an accurate assessment of the liver volume available for transplantation. In addition, the mutual principles of the procedures include dissection in the appropriate anatomic plane without portal occlusion, minimization of blood loss, and avoidance of injury to the remaining liver. It is essential first step to identify and evaluate the major hepatic vascular structure for liver surgical planning. In this paper, the threshold was determined to segment the liver region automatically based on the distribution ratio of intensity value; and the hepatic vessels were extracted with mathematical morphology transformation, which called hit operation, that is slightly modified version of hit-and-miss operation on contrast enhanced CT image sequence. We identified the vein using the preserved voxel connectivity between two consecutive transverse image sequences, followed by resection into right lobe including right hepatic vein, middle hepatic vein branches andleft lobe including left hepatic vein. An automated hepatic vessel segmentation scheme is recommended for liver surgical planning such as tumor resection and transplantation. These vessel extraction method combined with liver region segmentation technique could be applicable to extract tree-like organ structures such as carotid, renal, coronary artery, and airway path from various medical imaging modalities.

Hepatic vessels segmentation using deep learning and preprocessing enhancement

“…For several reasons, it is difficult to do quantitative comparisons with other segmentation approaches described in the literature. 13,19,31,[40][41][42][43][44][45][46] First, there are not many studies on this topic of liver vasculature segmentation. Some authors used their own clinical data sets, others employed artificial data sets, and still others confirmed their findings using images of created forms (phantoms) resembling veins.…”

“…For several reasons, it is difficult to do quantitative comparisons with other segmentation approaches described in the literature 13,19,31,40–46 . First, there are not many studies on this topic of liver vasculature segmentation.…”