Zhang Shaoting, Zhan Yiqiang, Dewan Maneesh, Huang Junzhou, Metaxas Dimitris N, Zhou Xiang Sean
Siemens Medical Solutions, Malvern, PA, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):451-8. doi: 10.1007/978-3-642-23629-7_55.
Appearance and shape are two key elements exploited in medical image segmentation. However, in some medical image analysis tasks, appearance cues are weak/misleading due to disease/artifacts and often lead to erroneous segmentation. In this paper, a novel deformable model is proposed for robust segmentation in the presence of weak/misleading appearance cues. Owing to the less trustable appearance information, this method focuses on the effective shape modeling with two contributions. First, a shape composition method is designed to incorporate shape prior on-the-fly. Based on two sparsity observations, this method is robust to false appearance information and adaptive to statistically insignificant shape modes. Second, shape priors are modeled and used in a hierarchical fashion. More specifically, by using affinity propagation method, our deformable surface is divided into multiple partitions, on which local shape models are built independently. This scheme facilitates a more compact shape prior modeling and hence a more robust and efficient segmentation. Our deformable model is applied on two very diverse segmentation problems, liver segmentation in PET-CT images and rodent brain segmentation in MR images. Compared to state-of-art methods, our method achieves better performance in both studies.
外观和形状是医学图像分割中利用的两个关键要素。然而，在一些医学图像分析任务中，由于疾病/伪影，外观线索较弱/具有误导性，常常导致错误的分割。本文提出了一种新颖的可变形模型，用于在存在弱/误导性外观线索的情况下进行鲁棒分割。由于外观信息不太可靠，该方法专注于有效的形状建模，有两个贡献。首先，设计了一种形状合成方法，以即时纳入形状先验。基于两个稀疏性观察结果，该方法对虚假外观信息具有鲁棒性，并能适应统计上不显著的形状模式。其次，形状先验以分层方式进行建模和使用。更具体地说，通过使用亲和传播方法，我们的可变形表面被划分为多个分区，在这些分区上独立构建局部形状模型。该方案有助于更紧凑的形状先验建模，从而实现更鲁棒和高效的分割。我们的可变形模型应用于两个非常不同的分割问题，PET-CT图像中的肝脏分割和MR图像中的啮齿动物脑部分割。与现有方法相比，我们方法在两项研究中均取得了更好的性能。
