Zhang Shaoting, Zhan Yiqiang, Zhou Yan, Uzunbas Mustafa, Metaxas Dimitris N
Department of Computer Science, Rutgers University, Piscataway, NJ, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):435-42. doi: 10.1007/978-3-642-33454-2_54.
The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient.
最近提出的稀疏形状合成（SSC）为形状先验建模开辟了一条新途径。SSC不是假设任何形状统计的参数模型，而是通过用训练库中形状的稀疏组合来近似一个形状实例（通常从外观线索中导出），动态地纳入形状先验。从理论上讲，通过在库中包含尽可能多的训练形状，可以提高SSC的建模能力。然而，这种策略在实践中面临两个限制。首先，由于SSC在运行时涉及迭代稀疏优化，库中包含的形状实例越多，SSC的运行时效率就越低。因此，一个紧凑且信息丰富的形状字典比一个大的形状库更可取。其次，在医学成像应用中，训练形状很少一次性出现。每次出现新的训练形状时重新构建形状字典非常耗时，有时甚至不可行。在本文中，我们提出一种在线学习方法来解决这两个限制。我们的方法首先使用K-SVD算法构建一个初始形状字典。当新的训练形状出现时，我们不是从头开始重新构建字典，而是使用块坐标下降法更新现有的字典。使用动态更新的字典，稀疏形状合成可以在不牺牲运行时效率的情况下，优雅地扩展以从大量训练形状中建模形状先验。我们的方法在X射线肺部定位和MRI时间序列心脏分割上得到了验证。与原始的SSC相比，它表现出相当的性能，同时效率显著提高。
