Automatic Segmentation of Neonatal Brain MRI
Automatic Segmentation of Neonatal Brain MRI
复制标题
新生儿脑 MRI 自动分割
DOI：
10.1007/978-3-540-30135-6_2
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发表时间：
2004
影响因子：
5.4
通讯作者：
G. Gerig
中科院分区：
文献类型：
--
作者：
M. Prastawa;J. Gilmore;Weili Lin;G. Gerig
本文介绍了一种用于新生儿MRI的自动组织分割方法。新生儿脑MRI的分析和研究是非常感兴趣的，由于其潜在的研究早期生长模式和形态学变化的神经发育障碍。这些图像的自动分割是一项具有挑战性的任务，主要是由于低强度对比度和白色物质强度的不均匀性，其中白色物质可以分为早期髓鞘形成区域和非髓鞘形成区域。髓鞘形成的程度是分数体素性质，其表示作为年龄的函数的白色物质的区域变化。我们的方法利用注册的概率脑图谱来选择训练样本，并将其用作空间先验。该方法首先利用图聚类和鲁棒估计来估计初始强度分布。然后将估计值与空间先验一起用于执行偏差校正。最后，该方法使用样本修剪和非参数密度估计细化分割。初步结果表明，该方法能够分割主要的大脑结构，识别早期髓鞘形成区域和无髓鞘区域。
This paper describes an automatic tissue segmentation method for neonatal MRI. The analysis and study of neonatal brain MRI is of great interest due to its potential for studying early growth patterns and morphologic change in neurodevelopmental disorders. Automatic segmentation of these images is a challenging task mainly due to the low intensity contrast and the non-uniformity of white matter intensities, where white matter can be divided into early myelination regions and non-myelinated regions. The degree of myelination is a fractional voxel property that represents regional changes of white matter as a function of age. Our method makes use of a registered probabilistic brain atlas to select training samples and to be used as a spatial prior. The method first uses graph clustering and robust estimation to estimate the initial intensity distributions. The estimates are then used together with the spatial priors to perform bias correction. Finally, the method refines the segmentation using sample pruning and non-parametric density estimation. Preliminary results show that the method is able to segment the major brain structures, identifying early myelination regions and non-myelinated regions.