Contour tracking in echocardiographic sequences via sparse representation and dictionary learning.
Contour tracking in echocardiographic sequences via sparse representation and dictionary learning.
复制标题
DOI：
10.1016/j.media.2013.10.012
复制
发表时间：
2014-02
影响因子：
10.9
通讯作者：
Duncan, James S.
中科院分区：
文献类型：
--
作者：
Huang, Xiaojie;Dione, Donald P.;Compas, Colin B.;Papademetris, Xenophon;Lin, Ben A.;Bregasi, Alda;Sinusas, Albert J.;Staib, Lawrence H.;Duncan, James S.
本文提出了一种基于稀疏表示和字典学习的动态外观模型，用于跟踪超声心动图序列中左心室的心内膜和心外膜轮廓。我们利用个体数据固有的时空相干性来约束心脏轮廓估计，而不是从数据库中离线学习时空先验。轮廓跟踪器通过手动跟踪第一帧来初始化。它采用局部图像外观的多尺度稀疏表示，并在boosting框架中学习在线多尺度外观字典，同时对图像序列进行逐帧顺序分割。多尺度外观字典的权重自动优化。我们基于区域的水平集分割集成了一系列互补的多级信息，包括强度，多尺度局部外观和动态形状预测。该方法是有效的26个4D犬超声心动图图像采集健康和梗死后犬。分割结果与专家手工描记吻合较好。射血分数估计值也显示出与手动结果的良好一致性。我们的方法的优点是通过与传统的纯强度模型，基于配准的轮廓跟踪器，和最先进的数据库相关的离线动态形状模型的比较证明。我们还通过将该方法应用于四个4D人体数据集，证明了临床应用的可行性。
This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint cardiac contour estimation. The contour tracker is initialized with a manual tracing of the first frame. It employs multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame-by-frame sequentially. The weights of multiscale appearance dictionaries are optimized automatically. Our region-based level set segmentation integrates a spectrum of complementary multilevel information including intensity, multiscale local appearance, and dynamical shape prediction. The approach is validated on twenty-six 4D canine echocardiographic images acquired from both healthy and post-infarct canines. The segmentation results agree well with expert manual tracings. The ejection fraction estimates also show good agreement with manual results. Advantages of our approach are demonstrated by comparisons with a conventional pure intensity model, a registration-based contour tracker, and a state-of-the-art database-dependent offline dynamical shape model. We also demonstrate the feasibility of clinical application by applying the method to four 4D human data sets.
登录
查看更多内容
影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
影响因子：
10.6
作者：
Bosch, JG;Mitchell, SC;Reiber, JHC
通讯作者：
Reiber, JHC
影响因子：
10.6
作者：
Chalana, V;Linker, DT;Kim, YM
通讯作者：
Kim, YM
影响因子：
10.6
作者：
Dias, JMB;Leitao, JMN
通讯作者：
Leitao, JMN
影响因子：
1.1
作者：
Freund, Y;Schapire, RE
通讯作者：
Schapire, RE