Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion
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DOI：
10.1109/tmi.2015.2418298
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发表时间：
2015-09-01
影响因子：
10.6
通讯作者：
Ourselin, Sebastien
中科院分区：
文献类型：
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作者：
Cardoso, M. Jorge;Modat, Marc;Ourselin, Sebastien
临床注释，如体素二进制或概率组织分割、结构分割、感兴趣的病理区域和解剖标志是许多临床研究的关键。然而，由于手动生成这些注释的耗时特性，它们往往很少，并且仅限于较小的数据子集。这项工作探索了一种新的框架，通过中间步骤扩散和映射可用的示例，在形态不同的图像之间传播体素注释。在图像数据库上引入了连接形态相似的对象的空间变化的图形结构，使得即使在存在大范围的形态变化的情况下，信息也能够逐渐扩散到所有对象。我们通过两个例子说明了所提出的框架的实用性：使用分类标签的大脑分割和使用概率特征的组织分割。将所提出的方法应用到分类标签融合中，与最先进的方法相比，在统计上有非常显著的改进。当将所提出的框架应用于合成数据和真实数据的概率组织分割时，也观察到显著的改进，主要是在存在大的形态变异性的情况下。
Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regions-of-interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability.