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Gou L, Wang Y, Gao L, Zhong Y, Xie L, Wang H, Zha X, Shao Y, Xu H, Xu X, Yan J. Gapr for large-scale collaborative single-neuron reconstruction. Nat Methods 2024:10.1038/s41592-024-02345-z. [PMID: 38961277 DOI: 10.1038/s41592-024-02345-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
Whole-brain analysis of single-neuron morphology is crucial for unraveling the complex structure of the brain. However, large-scale neuron reconstruction from terabyte and even petabyte data of mammalian brains generated by state-of-the-art light microscopy is a daunting task. Here, we developed 'Gapr' (Gapr accelerates projectome reconstruction) that streamlines deep learning-based automatic reconstruction, 'automatic proofreading' that reduces human workloads at high-confidence sites, and high-throughput collaborative proofreading by crowd users through the Internet. Furthermore, Gapr offers a seamless user interface that ensures high proofreading speed per annotator, on-demand conversion for handling large datasets, flexible workflows tailored to diverse datasets and rigorous error tracking for quality control. Finally, we demonstrated Gapr's efficacy by reconstructing over 4,000 neurons in mouse brains, revealing the morphological diversity in cortical interneurons and hypothalamic neurons. Here, we present Gapr as a solution for large-scale single-neuron reconstruction projects.
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Affiliation(s)
- Lingfeng Gou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yanzhi Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Shanghai, China
| | - Le Gao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yiting Zhong
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Shanghai, China
| | - Lucheng Xie
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Haifang Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xi Zha
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yinqi Shao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Huatai Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
| | - Xiaohong Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
| | - Jun Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
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Abstract
Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust.
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Abstract
The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing. This paper surveys the latest neuron tracing methods available in the scientific literature as well as a selection of significant older papers to better place these proposals into context. They are categorized into global processing, local processing and meta-algorithm approaches. Furthermore, we point out the algorithmic components used to design each method and we report information on the datasets and the performance metrics used.
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