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首页> 外文期刊>Medical image analysis >Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm.
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Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm.

机译：使用概率图谱和EM算法对4D心脏MR图像进行分割。

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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.
机译：本文提出了一种基于Atlas的4D心脏MR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。概率性心脏图谱是根据14位健康志愿者的3D心脏图像序列的手动分割而构建的。它提供了左右心室，心肌和背景结构（例如肝脏，胃，肺和皮肤）的时空概率图。除了使用概率性心脏图谱作为先验信息之外，分割算法还使用4D马尔可夫随机场合并了空间和时间上下文信息。分类后，使用全局连接性过滤器提取每个结构的最大连接部分，这可以显着改善结果，尤其是对于心肌。针对手动分割进行了验证，并计算了249个3D体积上的手动分割与自动分割之间的相关性。我们使用“留一法”测试，在该测试中，要分割的图像集未用于构建其对应的图集。结果表明，该程序可以成功分割左心室（LV）（r = 0.96），心肌（r = 0.92）和右心室（r = 0.92）。此外，当由志愿者构建的图集模糊时，还可以对10例肥厚型心肌病患者的4D图像进行手动和自动分割，从而在LV（r = 0.93）和心肌（0.94）的体积中产生良好的相关性。

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