Guo Yi, Wang Yuanyuan, Nie Siqing, Yu Jinhua, Chen Ping
IEEE Trans Biomed Eng. 2014 Apr;61(4):1121-33. doi: 10.1109/TBME.2013.2295376.
A novel approach is presented to automatically segment the left ventricle in fetal echocardiograms. The proposed approach strategically integrates sparse representation, global constraint, and local refinement algorithms into an active appearance model (AAM) framework. In the training stage, we construct an enhanced AAM texture model to deal with the speckle and texture ambiguities. In the segmentation stage, the initial pose is located by a sparse representation method. Globally constrained points and local features with high clinical relevance are effectively incorporated into the AAM framework to make the model converge toward a desired position. Our proposed approach has been compared with the traditional ASM, the traditional AAM, and the globally constrained AAM on the synthetic and clinical data. The results show that compared with experts drawn contours, the overall segmentation accuracy of overlapped area in the synthetic and clinical images are 84.12% and 84.39%, respectively, superior to those of the other three methods. The experiments demonstrate that sparse representative methods greatly facilitate the initializations and our approach is capable of detecting the fetal left ventricle effectively, offering a better segmentation results.
本文提出了一种自动分割胎儿超声心动图中左心室的新方法。该方法将稀疏表示、全局约束和局部细化算法策略性地集成到主动外观模型（AAM）框架中。在训练阶段，我们构建了一个增强的AAM纹理模型来处理斑点和纹理模糊性。在分割阶段，通过稀疏表示方法确定初始姿态。具有高临床相关性的全局约束点和局部特征被有效地纳入AAM框架，使模型朝着期望位置收敛。我们提出的方法已在合成数据和临床数据上与传统主动形状模型（ASM）、传统AAM和全局约束AAM进行了比较。结果表明，与专家绘制的轮廓相比，合成图像和临床图像中重叠区域的总体分割准确率分别为84.12%和84.39%，优于其他三种方法。实验表明，稀疏表示方法极大地促进了初始化，并且我们的方法能够有效地检测胎儿左心室，提供更好的分割结果。
