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首页> 外文会议>6th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2003) Pt.II; Nov 15-18, 2003; Montreal, Canada >Assessing Early Brain Development in Neonates by Segmentation of High-Resolution 3T MRI
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Assessing Early Brain Development in Neonates by Segmentation of High-Resolution 3T MRI

机译：通过高分辨率3T MRI分割评估新生儿的早期大脑发育

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This paper describes effort towards automatic tissue segmentation in neonatal MRI. Extremely low contrast to noise ratio (CNR), regional intensity changes due to RF coil inhomogeneity and biology, and tissue property changes due to the early myelination and axon pruning processes require a methodology that combines the strength of spatial priors (template atlas), data modelling, and prior knowledge about brain development. We use an EM-type algorithm that includes tissue classification, inhomogeneity correction and brain stripping into an iterative optimization scheme using a mixture distribution model. A statistical brain atlas registered to the subject image serves as a spatial prior. White matter in neonates is modeled as a mixture model of non-myelinated and myelinated regions. A pilot study on 10 neonates demonstrates the feasibility of high-resolution neonatal MRI and of automatic tissue segmentation. Results demonstrate that interleaved segmentation and inhomogeneity correction, guided by a statistical spatial prior, will provide a powerful and efficient segmentation framework for this type of imaging data. It is demonstrated that the mixture model for white matter allows us to segment early myelination regions of the projection tract up to the motor cortex, while also providing non-myelinated white, gray and csf segmentation. The early myelination regions are hypothesized to develop early but have not yet been shown in quantitative MRI studies.
机译：本文介绍了新生儿MRI中自动组织分割的工作。与噪声比（CNR）的对比度极低，由于RF线圈不均匀和生物学而导致的区域强度变化，以及由于早期髓鞘化和轴突修剪过程而导致的组织特性变化，因此需要一种结合空间先验强度（模板图集），数据的方法建模以及关于大脑发育的先验知识。我们使用包括组织分类，不均匀性校正和脑剥离在内的EM型算法，使用混合分布模型将其转化为迭代优化方案。注册到对象图像的统计脑图谱充当空间先验。新生儿中的白质被建模为非髓鞘和髓鞘区域的混合模型。对10名新生儿进行的一项初步研究证明了高分辨率新生儿MRI和自动组织分割的可行性。结果表明，在统计空间先验的指导下，交错分割和不均匀性校正将为此类成像数据提供强大而有效的分割框架。事实证明，白质的混合模型使我们能够分割出投影束的早期髓鞘区域直至运动皮层，同时还提供了无髓鞘的白色，灰色和csf分割。假设早期髓鞘形成区域较早发展，但尚未在定量MRI研究中显示。

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