Simultaneous Segmentation of Multiple Organs in Multi-Dimensional Medical Images

我们开发了一个人体解剖学的数字图谱和多器官的同步分割算法。人体解剖数字图谱是一个计算和统计数据库，包括有关人体解剖，病理和功能，如生理和运动功能的信息。本研究的重点是12个器官的三维上腹部CT图像，即食管，心脏，胃，肝脏，胆囊，胰腺，左，右肾，脾，门静脉，主动脉和下腔静脉。我们建立了1）显示存在概率的器官概率图谱，2）包括平均和特征形状的统计形状模型，以及3）特征的统计数据库，例如CT图像中的灰度值。我们还开发了一种基于人体解剖学数字图谱的12个腹部器官的同步分割算法。该算法由空间归一化、器官粗提取和器官精提取三部分组成。提出了一种分层空间归一化方法，先对大器官进行归一化，然后对空间变化较大的小器官进行归一化。粗提取过程基于最大后验概率法.该方法使用一种改进的EM算法，它使用的概率地图集作为先验信息的特征的概率分布参数估计。我们提出了一种基于相邻体素特征的后验概率。最后，提出了一种多水平集方法，该方法基于相邻器官之间的相互作用同时提取12个器官。我们将该算法应用于17例腹部CT容积，并证实Jaccard指数平均为75%，这是相当高的，与以前的方法相比。

We developed a digital atlas of human anatomy and a simultaneous segmentation algorithm for multiple organs. The digital atlas of human anatomy is a computational and statistical database including information about human anatomy, pathology and function, such as physiological and motility function. This study focused on twelve organs in three dimensional upper abdominal CT images, namely oesophagus, heart, stomach, liver, gallbladder, pancreas, left and right kidneys, spleen, portal vein, aorta, and inferior vena cava. We built 1) probabilistic atlases of the organs showing the probability of existence, 2) statistical shape model including average and eigen-shapes, and 3) statistical database of features, such as gray values in the CT images. We also developed a simultaneous segmentation algorithm of 12 abdominal organs based on the digital atlas of human anatomy. The algorithm consists of spatial normalization, rough extraction and fine extraction of the organs. A hierarchal spatial normalization process was proposed in this project, which normalized large organs first, then performed normalization for small organs whose spatial variation is large. The rough extraction process was based on maximum a posterior method. The method estimated probabilistic distribution parameters of features using a modified EM algorithm which used the probabilistic atlases as a priori information. We proposed a posterior probability based on features of neighboring voxels. Finally the proposed method performed a multiple level set method which extracted the twelve organs simultaneously based on mutual interaction between neighboring organs. We applied the proposed algorithm to abdominal CT volumes of 17 cases and confirmed that the Jaccard index was 75% on average which is quite high comparing to the previous method.