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Accurate and Consistent 4D Segmentation of Serial Infant Brain MR Images

机译：连续婴儿脑MR图像的准确一致的4D分割

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Accurate and consistent segmentation of infant brain MR images plays an important role in quantifying the early brain development, especially in longitudinal studies. However, due to rapid maturation and myelination of brain tissues in the first year of life, white-gray matter contrast undergoes dramatic changes. In fact, the contrast inverses around 6 months of age, where the white and gray matter tissues are isointense and hence exhibit the lowest contrast, posing significant challenges for segmentation algorithms. In this paper, we propose a novel longitudinally guided level set method for segmentation of serial infant brain MR images, acquired from 2 weeks up to 1.5 years of age. The proposed method makes optimal use of Tl, T2 and the diffusion weighted images for complimentary tissue distribution information to address the difficulty caused by the low contrast. A longitudinally consistent term, which constrains the distance across the serial images within a biologically reasonable range, is employed to obtain temporally consistent segmentation results. The proposed method has been applied on 22 longitudinal infant subjects with promising results.
机译：婴儿大脑MR图像的准确且一致的分割在量化早期大脑发育（尤其是在纵向研究中）方面起着重要作用。然而，由于生命第一年大脑组织的快速成熟和髓鞘化，白灰色物质的对比发生了戏剧性的变化。实际上，对比度大约在6个月大时反转，其中白质和灰质组织是等强度的，因此呈现出最低的对比度，这对分割算法提出了重大挑战。在本文中，我们提出了一种新颖的纵向引导水平集方法，用于分割连续两周至1.5岁的婴儿脑部MR图像。所提出的方法将T1，T2和扩散加权图像最佳地用于互补的组织分布信息，以解决由低对比度引起的困难。使用纵向一致的术语来将时间序列上的距离限制在生物学上合理的范围内，以获取时间上一致的分割结果。所提出的方法已应用于22名纵向婴儿受试者，结果令人满意。

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