CAD R&D, Siemens Healthcare, Malvern, PA, USA.
Med Image Anal. 2012 Jan;16(1):265-77. doi: 10.1016/j.media.2011.08.004. Epub 2011 Sep 5.
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.
器官形状在各种临床实践中起着重要作用，例如诊断、手术规划和治疗评估。它通常来源于医学图像中的低级外观线索。然而，由于疾病和成像伪影，低级外观线索可能很弱或具有误导性。在这种情况下，形状先验对于推断和细化由图像外观得出的形状变得至关重要。有效建模形状先验具有挑战性，原因有三：（1）形状变化复杂，不能总是通过参数概率分布进行建模；（2）由图像外观线索（输入形状）得出的形状实例可能存在较大误差；（3）如果输入形状的局部细节在训练数据中没有统计学意义，则难以保留。在本文中，我们提出了一种新颖的稀疏形状组合模型（SSC），以在统一框架内处理这三个挑战。在我们的方法中，从形状库中选择并组合一组稀疏的形状，以推断/细化输入形状。因此，先验信息是隐含地包含在其中的。我们的模型利用输入形状实例的两个稀疏性观察结果：（1）输入形状可以通过形状库中的形状的稀疏线性组合来近似表示；（2）输入形状的某些部分可能包含较大误差，但这种误差是稀疏的。我们的模型被表述为一个稀疏学习问题。通过 L1 范数松弛，它可以通过一种高效的期望最大化（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都表现出更好的性能。
