Université de Lyon, CREATIS, CNRS UMR5220, INSERM U1044, Université Lyon 1, INSA-LYON, France.
Med Image Anal. 2012 Feb;16(2):386-401. doi: 10.1016/j.media.2011.10.003. Epub 2011 Oct 31.
The segmentation of the myocardium in echocardiographic images is an important task for the diagnosis of heart disease. This task is difficult due to the inherent problems of echographic images (i.e. low contrast, speckle noise, signal dropout, presence of shadows). In this article, we propose a method to segment the whole myocardium (endocardial and epicardial contours) in 2D echographic images. This is achieved using a level-set model constrained by a new shape formulation that allows to model both contours. The novelty of this work also lays in the fact that our framework allows to segment the whole myocardium for the four main views used in clinical routine. The method is validated on a dataset of clinical images and compared with expert segmentation.
超声心动图像中的心肌分割是心脏病诊断的重要任务。由于超声图像固有的问题（例如对比度低、斑点噪声、信号丢失、阴影存在），这项任务具有挑战性。在本文中，我们提出了一种在二维超声图像中分割整个心肌（心内膜和心外膜轮廓）的方法。这是通过使用水平集模型实现的，该模型受到一种新的形状公式的约束，该公式允许对两个轮廓进行建模。这项工作的新颖之处还在于，我们的框架允许对临床常规中使用的四个主要视图进行整个心肌的分割。该方法在临床图像数据集上进行了验证，并与专家分割进行了比较。
