Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
Neuroimage. 2018 Jun;173:88-112. doi: 10.1016/j.neuroimage.2018.01.054. Epub 2018 Jan 31.
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
人类发展连接组计划(dHCP)旨在创建首个生命早期的 4 维连接组图谱。深入了解这一连接组图谱,可能有助于我们理解正常和异常的大脑发育模式。该计划遵循明尼苏达大学人类连接组计划(HCP)所采用的既定最佳实践,以及由 FreeSurfer 开创的方法,利用皮质表面处理流水线。在本文中,我们提出了一种针对新生儿大脑结构磁共振成像(MRI)的全自动处理流水线。该流水线由皮质和皮质下体积分割、皮质表面提取和皮质表面膨胀的精细框架组成,专门针对 MRI 成像中成人和新生儿大脑之间的显著差异进行了设计。使用我们提出的流水线,我们的结果表明,可以对 465 名从孕龄 28 周到 45 周(PMA)的被试者的图像进行全自动处理;生成的皮质表面模型在拓扑上是正确的,并且在 85%的情况下与手动评估组织边界的结果非常吻合。结果优于最先进的新生儿组织分割模型,只有 2%的情况下出现了显著误差,这些误差与高运动的被试者相对应。在下游,这些表面将增强功能和弥散 MRI 数据集的比较,支持新兴的大脑连接模式建模。