| Literature DB >> 34887551 |
Lei Qu1,2,3, Yuanyuan Li1, Peng Xie2, Lijuan Liu2,4, Yimin Wang2,5, Jun Wu1, Yu Liu1, Tao Wang1, Longfei Li1, Kaixuan Guo1, Wan Wan1, Lei Ouyang1, Feng Xiong2, Anna C Kolstad6, Zhuhao Wu6,7, Fang Xu8, Yefeng Zheng9, Hui Gong10,11,12, Qingming Luo10,11,12,13, Guoqiang Bi3,8,14, Hongwei Dong15, Michael Hawrylycz16, Hongkui Zeng16, Hanchuan Peng17,18.
Abstract
Recent whole-brain mapping projects are collecting large-scale three-dimensional images using modalities such as serial two-photon tomography, fluorescence micro-optical sectioning tomography, light-sheet fluorescence microscopy, volumetric imaging with synchronous on-the-fly scan and readout or magnetic resonance imaging. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modal image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulting from different sample preparation methods and imaging modalities. We introduce a cross-modal registration method, mBrainAligner, which uses coherent landmark mapping and deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We build a brain atlas for the fluorescence micro-optical sectioning tomography modality to facilitate single-cell mapping, and used our method to generate a whole-brain map of three-dimensional single-neuron morphology and neuron cell types.Entities:
Mesh:
Year: 2021 PMID: 34887551 DOI: 10.1038/s41592-021-01334-w
Source DB: PubMed Journal: Nat Methods ISSN: 1548-7091 Impact factor: 47.990