IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 106 Mason Farm Road, Chapel Hill, NC 27599, USA.
Neuroimage. 2010 Jan 1;49(1):391-400. doi: 10.1016/j.neuroimage.2009.07.066. Epub 2009 Aug 4.
In the study of early brain development, tissue segmentation of neonatal brain MR images remains challenging because of the insufficient image quality due to the properties of developing tissues. Among various brain tissue segmentation algorithms, atlas-based brain image segmentation can potentially achieve good segmentation results on neonatal brain images. However, their performances rely on both the quality of the atlas and the spatial correspondence between the atlas and the to-be-segmented image. Moreover, it is difficult to build a population atlas for neonates due to the requirement of a large set of tissue-segmented neonatal brain images. To combat these obstacles, we present a longitudinal neonatal brain image segmentation framework by taking advantage of the longitudinal data acquired at late time-point to build a subject-specific tissue probabilistic atlas. Specifically, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic-atlas-based tissue segmentation, along with the longitudinal atlas reconstructed by the late time image of the same subject. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineations and two population-atlas-based segmentation methods. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.
在早期大脑发育的研究中，由于发育组织的特性，新生儿脑磁共振图像的组织分割仍然具有挑战性。在各种脑组织结构分割算法中，基于图谱的脑图像分割算法有可能对新生儿脑图像获得良好的分割效果。然而，它们的性能依赖于图谱的质量和图谱与待分割图像之间的空间对应关系。此外，由于需要大量组织分割的新生儿脑图像，因此很难为新生儿建立一个群体图谱。为了克服这些障碍，我们利用晚期时间点采集的纵向数据构建了一个基于主题的组织概率图谱，提出了一个纵向新生儿脑图像分割框架。具体来说，新生儿脑的组织分割被表述为两个迭代步骤：偏置校正和基于概率图谱的组织分割，以及通过同一主题的晚期图像重建的纵向图谱。该方法通过视觉检查进行了定性评估，并通过与手动勾画和两种基于群体图谱的分割方法进行比较进行了定量评估。实验结果表明，利用基于主题的概率图谱可以显著提高新生儿脑图像的组织分割。
