GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population
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SOFTWARE ORIGINAL ARTICLE
GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population Hang Zhou 1,2 & Shiwei Li 1,2 & Anan Li 1,2 & Qing Huang 1,2 & Feng Xiong 1,2 & Ning Li 1,2 & Jiacheng Han 1,2 & Hongtao Kang 1,2 & Yijun Chen 1,2 & Yun Li 1,2 & Huimin Lin 1,2 & Yu-Hui Zhang 1,2 & Xiaohua Lv 1,2 & Xiuli Liu 1,2 & Hui Gong 1,2 & Qingming Luo 1,2 & Shaoqun Zeng 1,2 & Tingwei Quan 1,2,3
# Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Neuronal morphology . Neuron reconstruction at brain-wide scale . Fast localization of errors
1. Introduction Mapping neuronal morphology at the single-cell level not only bridges the gap between micro-scale and macro-scale studies (Lichtman and Denk 2011; DeFelipe 2010; Bohland et al. 2009) of brain networks but also plays an important ro le in
Hang Zhou, Shiwei Li and Anan Li equally contributed to this work. Electronic Supplementary Material The online version of this article (https://doi.org/10.1007/s12021-020-09484-6) contains supplementary material, which is available to authorized users. * Tingwei Quan [email protected] 1
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei Wuhan 430074, China
2
MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei Wuhan 430074, China
3
School of Mathematics and Economics, Hubei University of Education, 430205 Wuhan, Hubei, China
studies of cell type, neural circuits, and neural computing (Meijering 2010; Donohue and Ascoli 2011; Peng et al. 2015a, b). Recent breakthroughs in imaging (Li et al. 2010; Ragan et al. 2012; Silvestri et al. 2012; Osten and Margrie 2013) and molecular labeling (Chung and Deisseroth 2013; Jeffer
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