IEEE Trans Biomed Eng. 2013 Oct;60(10):2887-95. doi: 10.1109/TBME.2013.2266118. Epub 2013 Jun 4.
Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this paper, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardium and then segmenting the epicardium. To this end, the endocardium is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result. The robustness and accuracy of the proposed approach is demonstrated by experimental results from 33 human and 12 pig CT images.
心脏疾病的预后和诊断通常需要定量评估心室容积、质量和射血分数。这些评估都涉及到心肌壁的描绘，由于心肌形状和图像质量的巨大差异，这是一项具有挑战性的任务。在本文中，我们提出了一种从心脏 CT 图像中自动提取左、右心室心肌壁的方法。该方法依次定位左、右心室，其中每个心室首先通过识别心内膜然后分割心外膜来检测。为此，利用从 CT 图像在线获得的几何特征来定位心内膜。之后，采用变分区域生长模型提取心室的心外膜。具体来说，通过在血池表面上使用主动轮廓模型来确定左心室的心内膜位置。为了定位右心室，主动轮廓模型应用于基于左心室分割结果提取的心脏表面上。通过 33 个人类和 12 个猪 CT 图像的实验结果证明了所提出方法的鲁棒性和准确性。
