Shen Zhengwen, Gao Yuanyuan, Zhang Yu
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Apr;33(2):295-302.
Lung four dimensional computed tomography(4D-CT)is of great value in tumor target localization and precise cancer radiotherapy.However,it is hard to segment tumors in 4D-CT data manually,since the data may contain a great number of slices with tumor.Meanwhile,auto-segmentation does not certainly guarantee the accuracy due to the complexity of images.Therefore,a new automatic segmentation technique based on Graph Cuts with star shape prior was proposed to increase automation and guarantee the accuracy of segmentation in our laboratory.Firstly,an object seed was selected in the image of initial phase and an initial target block was formed centering the selected seed.Then,the full search block-matching algorithm was adopted to obtain the most similar target block in the next phase and compute the motion field between them,and so on.Afterwards,the center seeds of each phase were obtained according to the motion fields,which would be set to the center point of star shape prior.Finally,tumors could be automatically segmented with Graph Cuts algorithm and star shape prior.Both qualitative and quantitative evaluation results showed that our approach could not only guarantee the accuracy of segmentation but also increase automation,compared with the traditional Graph Cuts algorithm.
肺部四维计算机断层扫描（4D-CT）在肿瘤靶区定位和精确癌症放疗中具有重要价值。然而，手动分割4D-CT数据中的肿瘤很困难，因为数据可能包含大量带有肿瘤的切片。同时，由于图像的复杂性，自动分割不一定能保证准确性。因此，我们实验室提出了一种基于带星型先验的图割的新自动分割技术，以提高自动化程度并保证分割的准确性。首先，在初始相位图像中选择一个目标种子，并以所选种子为中心形成一个初始目标块。然后，采用全搜索块匹配算法在下一相位中获取最相似的目标块并计算它们之间的运动场，依此类推。之后，根据运动场获得各相位的中心种子，将其设置为星型先验的中心点。最后，利用图割算法和星型先验自动分割肿瘤。定性和定量评估结果均表明，与传统图割算法相比，我们的方法不仅能保证分割的准确性，还能提高自动化程度。
