Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix
Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix
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基于置信加权概率矩阵的混合图谱森林在MR图像中自动分割脑组织
DOI：
10.1002/ima.22301
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发表时间：
2019
影响因子：
3.3
通讯作者：
Hung Chih Cheng
中科院分区：
文献类型：
--
作者：
Xu Lijun;Liu Hong;Song Enmin;Jin Renchao;Hung Chih Cheng
对MR脑图像中的特定组织进行分割并进行定量分析，可以辅助疾病诊断和医学研究。因此，需要一种鲁棒且准确的自动分割方法。基于图谱的方法是一种常见且有效的自动分割方法，其中图谱是指由强度图像及其相应的标签图像组成的一对图像。除了一般的基于多图集的方法，通过单个图集传播标签然后融合它们之外，我们提出了一种基于置信加权概率矩阵的混合图集森林，将图集集视为一个整体，并对每个体素进行不同的处理。在该框架中，我们首先将图谱配准到目标图像空间，并计算配准图谱中体素的置信度。然后，生成置信加权概率矩阵，并将其增强到图谱或目标的强度图像，以提供目标组织的空间信息。第三，训练混合图谱森林以收集数据集中的图谱之间的特征和相关性信息。最后，利用训练好的混合图谱森林对目标组织的分割进行预测。在两个公开数据集上对该方法的分割性能和组件效率进行了评估。实验结果和定量比较表明，该方法能够有效地提取图像的空间信息和相关性，从而获得准确的分割结果。
The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas‐based‐method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi‐atlas‐based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence‐weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence‐weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.