Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany.
Med Image Anal. 2013 Feb;17(2):236-53. doi: 10.1016/j.media.2012.10.005. Epub 2012 Dec 5.
In this paper, a new segmentation framework with prior knowledge is proposed and applied to the left ventricles in cardiac Cine MRI sequences. We introduce a new formulation of the random walks method, coined as guided random walks, in which prior knowledge is integrated seamlessly. In comparison with existing approaches that incorporate statistical shape models, our method does not extract any principal model of the shape or appearance of the left ventricle. Instead, segmentation is accompanied by retrieving the closest subject in the database that guides the segmentation the best. Using this techniques, rare cases can also effectively exploit prior knowledge from few samples in training set. These cases are usually disregarded in statistical shape models as they are outnumbered by frequent cases (effect of class population). In the worst-case scenario, if there is no matching case in the database to guide the segmentation, performance of the proposed method reaches to the conventional random walks, which is shown to be accurate if sufficient number of seeds is provided. There is a fast solution to the proposed guided random walks by using sparse linear matrix operations and the whole framework can be seamlessly implemented in a parallel architecture. The method has been validated on a comprehensive clinical dataset of 3D+t short axis MR images of 104 subjects from 5 categories (normal, dilated left ventricle, ventricular hypertrophy, recent myocardial infarction, and heart failure). The average segmentation errors were found to be 1.54 mm for the endocardium and 1.48 mm for the epicardium. The method was validated by measuring different algorithmic and physiologic indices and quantified with manual segmentation ground truths, provided by a cardiologist.
本文提出了一种新的基于先验知识的分割框架，并将其应用于心脏 Cine MRI 序列的左心室。我们引入了一种新的随机游走方法的公式，称为引导随机游走，其中无缝地集成了先验知识。与现有的结合统计形状模型的方法相比，我们的方法不会提取左心室的形状或外观的任何主要模型。相反，分割伴随着从数据库中检索最接近的个体，该个体最佳地指导分割。使用这种技术，罕见的病例也可以有效地利用训练集中少数样本的先验知识。这些病例在统计形状模型中通常被忽略，因为它们被频繁病例（类别群体的影响）所淹没。在最坏的情况下，如果数据库中没有匹配的病例来指导分割，那么所提出方法的性能达到传统的随机游走，这被证明是准确的，如果提供足够数量的种子。通过稀疏线性矩阵运算可以快速求解所提出的引导随机游走，整个框架可以无缝地在并行架构中实现。该方法已经在 104 名来自 5 个类别的患者的 3D+t 短轴磁共振图像的综合临床数据集上进行了验证（正常、左心室扩张、心室肥厚、近期心肌梗死和心力衰竭）。心内膜的平均分割误差为 1.54mm，心外膜的平均分割误差为 1.48mm。该方法通过测量不同的算法和生理指标并与心脏病专家提供的手动分割地面真实进行定量验证。
