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A Large Deformation Diffeomorphic Approach to Registration of CLARITY Images via Mutual Information

机译:大变形微分方法通过互信息配准CLARITY图像

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CLARITY is a method for converting biological tissues into translucent and porous hydrogel-tissue hybrids. This facilitates interrogation with light sheet microscopy and penetration of molecular probes while avoiding physical slicing. In this work, we develop a pipeline for registering CLARIfied mouse brains to an annotated brain atlas. Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases. Thus we adopt a large deformation diffeomorphic approach for registering images via mutual information matching. Furthermore we show how a cascaded multi-resolution approach can improve registration quality while reducing algorithm run time. As acquired image volumes were over a terabyte in size, they were far too large for work on personal computers. Therefore the NeuroData computational infrastructure was deployed for multi-resolution storage and visualization of these images and aligned annotations on the web.
机译:透明度是一种将生物组织转化为半透明和多孔的水凝胶-组织杂交体的方法。这有利于用光片显微镜进行询问和分子探针的渗透,同时避免了物理切片。在这项工作中,我们开发了一个管道,用于将CLARIfied小鼠的大脑注册到带注释的大脑图集。由于这种显微镜技术的新颖性,使用绝对强度值将这些图像与现有标准图谱对齐是不切实际的。因此,我们采用大变形微分方法通过相互信息匹配来配准图像。此外,我们展示了级联多分辨率方法如何在减少算法运行时间的同时提高注册质量。由于获取的图像量超过TB,因此它们太大了,无法在个人计算机上工作。因此,NeuroData计算基础架构被部署为这些图像以及网络上对齐的注释的多分辨率存储和可视化。

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