Zhou Xiangrong, Kitagawa Teruhiko, Hara Takeshi, Fujita Hiroshi, Zhang Xuejun, Yokoyama Ryujiro, Kondo Hiroshi, Kanematsu Masayuki, Hoshi Hiroaki
Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu 501-1194, Japan.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):856-63. doi: 10.1007/11866763_105.
A probabilistic model was proposed in this research for fully-automated segmentation of liver region in non-contrast X-ray torso CT images. This probabilistic model was composed of two kinds of probability that show the location and density (CT number) of the liver in CT images. The probability of the liver on the spatial location was constructed from a number of CT scans in which the liver regions were pre-segmented manually as gold standards. The probability of the liver on density was estimated specifically using a Gaussian function. The proposed probabilistic model was used for automated liver segmentation from non-contrast CT images. 132 cases of the CT scans were used for the probabilistic model construction and then this model was applied to segment liver region based on a leave-one-out method. The performances of the probabilistic model were evaluated by comparing the segmented liver with the gold standard in each CT case. The validity and usefulness of the proposed model were proved.
本研究提出了一种概率模型，用于在非增强胸部CT图像中全自动分割肝脏区域。该概率模型由两种概率组成，分别表示CT图像中肝脏的位置和密度（CT值）。肝脏在空间位置上的概率是根据一些CT扫描构建的，在这些扫描中，肝脏区域已被手动预分割为金标准。肝脏密度的概率是使用高斯函数专门估计的。所提出的概率模型用于从非增强CT图像中自动分割肝脏。132例CT扫描用于概率模型构建，然后基于留一法应用该模型分割肝脏区域。通过将每个CT病例中分割出的肝脏与金标准进行比较，评估了概率模型的性能。所提出模型的有效性和实用性得到了证明。
