Lorenzo-Valdés Maria, Sanchez-Ortiz Gerardo I, Elkington Andrew G, Mohiaddin Raad H, Rueckert Daniel
Visual Information Processing Group, Department of Computing, Imperial College London, 180 Queens' Gate, London SW7 2BZ, UK.
Med Image Anal. 2004 Sep;8(3):255-65. doi: 10.1016/j.media.2004.06.005.
In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate a priori information into the classification process. The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 healthy volunteers. It provides space and time-varying probability maps for the left and right ventricles, the myocardium, and background structures such as the liver, stomach, lungs and skin. In addition to using the probabilistic cardiac atlas as a priori information, the segmentation algorithm incorporates spatial and temporal contextual information by using 4D Markov Random Fields. After the classification, the largest connected component of each structure is extracted using a global connectivity filter which improves the results significantly, especially for the myocardium. Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. We used the 'leave one out' test where the image set to be segmented was not used in the construction of its corresponding atlas. Results show that the procedure can successfully segment the left ventricle (LV) (r = 0.96), myocardium (r = 0.92) and right ventricle (r = 0.92). In addition, 4D images from 10 patients with hypertrophic cardiomyopathy were also manually and automatically segmented yielding a good correlation in the volumes of the LV (r = 0.93) and myocardium (0.94) when the atlas constructed with volunteers is blurred.
本文提出了一种基于图谱的4D心脏磁共振图像自动分割算法。该算法基于期望最大化（EM）算法的4D扩展。EM算法使用4D概率心脏图谱来估计初始模型参数，并将先验信息整合到分类过程中。概率心脏图谱是由14名健康志愿者的3D心脏图像序列的手动分割构建而成。它为左心室、右心室、心肌以及肝脏、胃、肺和皮肤等背景结构提供时空变化的概率图。除了将概率心脏图谱用作先验信息外，分割算法还通过使用4D马尔可夫随机场纳入空间和时间上下文信息。分类后，使用全局连通性滤波器提取每个结构的最大连通分量，这显著改善了结果，尤其是对于心肌。计算了针对249个3D体积的手动分割的验证以及手动分割与自动分割之间的相关性。我们使用了“留一法”测试，其中要分割的图像集未用于构建其相应的图谱。结果表明，该过程能够成功分割左心室（LV）（r = 0.96）、心肌（r = 0.92）和右心室（r = 0.92）。此外，对10例肥厚型心肌病患者的4D图像也进行了手动和自动分割，当用志愿者构建的图谱模糊处理时，左心室（r = 0.93）和心肌（0.94）的体积具有良好的相关性。
