Lu Xuesong, Yang Rongqian, Xie Qinlan, Ou Shanxing, Zha Yunfei, Wang Defeng
College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, People's Republic of China.
School of Materials Science and Engineering, South China University of Technology, Guangzhou, 510006, People's Republic of China.
Biomed Eng Online. 2017 Mar 28;16(1):39. doi: 10.1186/s12938-017-0323-1.
Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images.
In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects.
We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 ([Formula: see text]) for left ventricle myocardium and from 0.6307 to 0.6519 ([Formula: see text]) for right atrium.
The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.
双源计算机断层扫描(DSCT)是诊断和治疗心脏病的一种非常有效的方法。DSCT时空图像的定量信息对于评估心脏功能可能很重要。为避免手动勾勒的缺点,开发一种用于4D心脏图像的自动分割技术势在必行。
在本文中,我们基于非刚性配准实现了心脏分割-传播框架。使用n维尺度不变特征变换方法的扩展来提取解剖亚结构的对应点。它们被视为使用自由形式变形的非刚性配准的约束项,以抑制个体之间的大变化和边界模糊性。
我们在15名患者的十个时间阶段验证了我们的方法。图谱由来自十名患者的训练数据集构建。在其余数据上,与原始互信息相比,中位数重叠显示有显著改善,特别是左心室心肌从0.4703提高到0.5015([公式:见正文]),右心房从0.6307提高到0.6519([公式:见正文])。
所提出的方法优于仅基于强度的标准互信息。左心室心肌和右心房的分割误差已显著降低。使用我们的框架,整个心脏的平均表面距离约为1.73毫米。