| Literature DB >> 32844332 |
Hang Zhou1,2, Shiwei Li1,2, Anan Li1,2, Qing Huang1,2, Feng Xiong1,2, Ning Li1,2, Jiacheng Han1,2, Hongtao Kang1,2, Yijun Chen1,2, Yun Li1,2, Huimin Lin1,2, Yu-Hui Zhang1,2, Xiaohua Lv1,2, Xiuli Liu1,2, Hui Gong1,2, Qingming Luo1,2, Shaoqun Zeng1,2, Tingwei Quan3,4,5.
Abstract
Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.Entities:
Keywords: Fast localization of errors; Neuron reconstruction at brain-wide scale; Neuronal morphology
Year: 2021 PMID: 32844332 DOI: 10.1007/s12021-020-09484-6
Source DB: PubMed Journal: Neuroinformatics ISSN: 1539-2791