Sun W, Qetin M, Chan R, Reddy V, Holmvang G, Chandar V, Willsky A
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.
Inf Process Med Imaging. 2005;19:553-65. doi: 10.1007/11505730_46.
Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain heart conditions. Existing LV segmentation techniques are founded mostly upon algorithms for segmenting static images. In order to exploit the dynamic structure of the heart in a principled manner, we approach the problem of LV segmentation as a recursive estimation problem. In our framework, LV boundaries constitute the dynamic system state to be estimated, and a sequence of observed cardiac images constitute the data. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past segmentations. This requires a dynamical system model of the LV, which we propose to learn from training data through an information-theoretic approach. To incorporate the learned dynamic model into our segmentation framework and obtain predictions, we use ideas from particle filtering. Our framework uses a curve evolution method to combine such predictions with the observed images to estimate the LV boundaries at each time. We demonstrate the effectiveness of the proposed approach on a large set of cardiac images. We observe that our approach provides more accurate segmentations than those from static image segmentation techniques, especially when the observed data are of limited quality.
在整个心动周期中获得准确的左心室（LV）分割结果可为某些心脏疾病的诊断提供有用的定量信息（如射血分数）和定性信息。现有的LV分割技术大多基于静态图像分割算法。为了以一种有原则的方式利用心脏的动态结构，我们将LV分割问题视为一个递归估计问题。在我们的框架中，LV边界构成待估计的动态系统状态，而一系列观察到的心脏图像构成数据。通过将该问题表述为状态估计问题，每个特定时刻的分割不仅基于该时刻观察到的数据，还基于基于过去分割结果的预测。这需要一个LV的动态系统模型，我们建议通过信息论方法从训练数据中学习该模型。为了将学习到的动态模型纳入我们的分割框架并获得预测结果，我们采用了粒子滤波的思想。我们的框架使用曲线演化方法将此类预测与观察到的图像相结合，以估计每个时刻的LV边界。我们在大量心脏图像上展示了所提出方法的有效性。我们观察到，我们的方法比静态图像分割技术提供了更准确的分割结果，特别是当观察到的数据质量有限时。
