Towards robust and effective shape modeling: Sparse shape composition
Towards robust and effective shape modeling: Sparse shape composition
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DOI：
10.1016/j.media.2011.08.004
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发表时间：
2012-01-01
影响因子：
10.9
通讯作者：
Zhou, Xiang Sean
中科院分区：
文献类型：
--
作者：
Zhang, Shaoting;Zhan, Yiqiang;Zhou, Xiang Sean
器官形状在各种临床实践中起着重要作用，例如，诊断、手术计划和治疗评估。它通常来自医学图像中的低水平外观线索。然而，由于疾病和成像伪影，低水平的外观线索可能是弱的或误导。在这种情况下，形状先验变得至关重要，以推断和细化的图像外观派生的形状。形状先验的有效建模是具有挑战性的，因为：（1）形状变化是复杂的，并且不能总是通过参数概率分布来建模;（2）从图像外观线索（输入形状）导出的形状实例可能具有显著误差;以及（3）如果输入形状的局部细节在训练数据中不具有统计学显著性，则难以保留它们。在本文中，我们提出了一种新的稀疏形状组合模型（SSC）来处理这三个挑战在一个统一的框架。在我们的方法中，形状存储库中的一组稀疏的形状被选择并组合在一起，以推断/细化输入形状。因此，先验信息被隐含地即时并入。我们的模型利用了输入形状实例的两个稀疏性观察：（1）输入形状可以近似地由形状存储库中的形状的稀疏线性组合表示;（2）输入形状的部分可能包含粗差，但这些误差是稀疏的。我们的模型被制定为一个稀疏学习问题。使用L1范数松弛，它可以通过有效的期望最大化（EM）类型的框架来解决。我们的方法在两个医学应用中得到了广泛的验证，即X射线图像中的2D肺部定位和低剂量CT扫描中的3D肝脏分割。与最先进的方法相比，我们的模型在这两项研究中表现出更好的性能。(C)2011 Elsevier B. V.保留所有权利。
Organ shape plays an important role in various clinical practices, e.g., diagnosis, surgical planning and treatment evaluation. It is usually derived from low level appearance cues in medical images. However, due to diseases and imaging artifacts, low level appearance cues might be weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived by image appearances. Effective modeling of shape priors is challenging because: (1) shape variation is complex and cannot always be modeled by a parametric probability distribution; (2) a shape instance derived from image appearance cues (input shape) may have gross errors; and (3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, a sparse set of shapes in the shape repository is selected and composed together to infer/refine an input shape. The a priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: (1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; (2) parts of the input shape may contain gross errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using L1 norm relaxation, it can be solved by an efficient expectation-maximization (EM) type of framework. Our method is extensively validated on two medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies. (C) 2011 Elsevier B.V. All rights reserved.