Integrated Program in Neuroscience, McGill University, Montreal, Canada.
Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.
Sci Data. 2018 Jun 19;5:180107. doi: 10.1038/sdata.2018.107.
Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.
先前我们小组的工作表明，可以使用多个输入图谱来改进多图谱框架（MAGeT-Brain），以提高基于对象的分割准确性。目前，纹状体、苍白球和丘脑的分割是从一个源自注释的连续组织学切片的单个高分辨率和高对比度 MRI 图谱中生成的。在这里，我们将该图谱变形到五个高分辨率 MRI 模板中，以创建五个全新的图谱。这项工作的总体目标是使用这些新变形的图谱作为 MAGeT-Brain 的输入，以努力整合和改进我们小组之前的论文中提出的工作流程，从而实现同时进行多结构分割。本工作介绍了使用先前提出的技术创建图谱的方法细节，其中修改了图谱标签以模拟 MRI 的强度和对比度分布，以方便图谱到模板的非线性变换估计。Dice 的 Kappa 度量用于演示图谱的高质量注册和分割准确性。最终的图谱可在 https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical 获得。
