Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation.
Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation.
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DOI：
10.1016/j.neuroimage.2010.02.025
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发表时间：
2010-06
期刊：
影响因子：
5.7
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Shi, Feng;Yap, Pew-Thian;Fan, Yong;Gilmore, John H.;Lin, Weili;Shen, Dinggang
新生儿脑MRI图像分割是一个具有挑战性的问题，其图像质量较差。基于图谱的分割方法已广泛应用于指导脑组织分割。现有的脑图谱通常是通过在人群中平均预分割图像来构建的。然而，这种方法降低了局部主体间结构的可变性，导致分割制导能力较低。为了解决这一问题，我们提出了一种基于地图集的新生儿大脑分割的多区域多参考框架。对于脑分割的每个区域，将空间归一化的预分割图像聚类到许多子种群中。一个区域的每个子种群代表一个独立的分布，从中可以生成区域概率图谱。这些区域地图集的选择，跨越不同的子区域，最终将自适应地组合起来，形成特定于查询图像的整体地图集。给定查询图像，通过将查询图像与每个子种群的参考或范例进行区域比较来确定适当的区域地图集。在获得整体地图集后，采用基于地图集的联合配准-分割策略对查询图像进行分割。由于该方法生成的图谱与查询图像的相似度明显高于传统的平均形状图谱，因此可以获得更好的组织分割结果。通过将所提出的方法应用于我们研究所提供的大量新生儿脑图像，验证了这一点。随机选取的10张新生儿脑图像的实验结果表明，与人工分割相比，该方法获得了更高的组织重叠率和更低的标准偏差（SDs），即GM为0.86 (SD 0.02)， WM为0.83 (SD 0.03)， CSF为0.80 （SD 0.05）。该方法也优于其他两种基于平均形状图谱的分割方法。
Neonatal brain MRI segmentation is a challenging problem due to its poor image quality. Atlas-based segmentation approaches have been widely used for guiding brain tissue segmentation. Existing brain atlases are usually constructed by equally averaging pre-segmented images in a population. However, such approaches diminish local inter-subject structural variability and thus lead to lower segmentation guidance capability. To deal with this problem, we propose a multi-region-multi-reference framework for atlas-based neonatal brain segmentation. For each region of a brain parcellation, a population of spatially normalized pre-segmented images is clustered into a number of sub-populations. Each sub-population of a region represents an independent distribution from which a regional probability atlas can be generated. A selection of these regional atlases, across different sub-regions, will in the end be adaptively combined to form an overall atlas specific to the query image. Given a query image, the determination of the appropriate set of regional atlases is achieved by comparing the query image regionally with the reference, or exemplar, of each sub-population. Upon obtaining an overall atlas, an atlas-based joint registration-segmentation strategy is employed to segment the query image. Since the proposed method generates an atlas which is significant more similar to the query image than the traditional average-shape atlas, better tissue segmentation results can be expected. This is validated by applying the proposed method on a large set of neonatal brain images available in our institute. Experimental results on a randomly selected set of 10 neonatal brain images indicate that the proposed method achieves higher tissue overlap rates and lower standard deviations (SDs) in comparison with manual segmentations, i.e., 0.86 (SD 0.02) for GM, 0.83 (SD 0.03) for WM, and 0.80 (SD 0.05) for CSF. The proposed method also outperforms two other average-shape atlas based segmentation methods.
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