基于图谱的肝脏CT三维自动分割研究

Atlas Based Automatic Liver 3D CT Image Segmentation

目的在肝脏外科手术或肝脏病理研究中,计算肝脏体积是重要步骤。由于肝脏外形复杂、临近组织灰度值与之接近等特点,肝脏的自动医学图像分割仍是医学图像处理中的难点之一。方法本文采用图谱结合3D非刚性配准的方法,同时加入肝脏区域搜索算法,实现了鲁棒性较高的肝脏自动分割程序。首先,利用20套训练图像创建图谱,然后程序自动搜索肝脏区域,最后将图谱与待分割CT图像依次进行仿射配准和B样条配准。配准以后的图谱肝脏轮廓即可表示为目标肝脏分割轮廓,进而计算出肝脏体积。结果评估结果显示,上述方法在肝脏体积误差方面表现出色,达到77分,但在局部(主要在肝脏尖端)出现较大的误差。结论该方法分割临床肝脏CT图像具有可行性。

Objective Liver segmentation is an important step for the planning and navigation in liver surgery. Accurate, fast and robust automatic segmentation methods for clinical routine data are urgently needed. Because of the liver＇ s characteristics, such as the complexity of the external form, the similarity between the intensities of the liver and the tissues around it, automatic segmentation of the liver is one of the difficulties in medical image processing. Methods In this paper,3D non-rigid registration from a refined atlas to liver CT images is used for segmentation. Firstly, twenty sets of training images are utilized to create an atlas. Then the liver initial region is searched and located automatically. After that threshold filtering is used to enhance the robustness of segmentation. Finally,this atlas is non-rigidly registered to the liver in CT images with affine and B-spline in succession. The registered segmentation of liver＇ s arias represented the segmentation of the target liver, and then the liver volume was calculated. Results The evaluation show that the proposed method works well in liver volume error,with the 77 score ,yet appears greater error in local position （mostly in liver tips）. Conclusions Experimental results show that this method is feasible for clinical liver CT image segmentation.