PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance
Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-...
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| Published in | Frontiers in neuroinformatics Vol. 15; p. 767936 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Research Foundation
28.01.2022
Frontiers Media S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1662-5196 1662-5196 |
| DOI | 10.3389/fninf.2021.767936 |
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| Abstract | Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure. |
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| AbstractList | Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure. Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of a user-friendly software of calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure. Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure.Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure. |
| Author | Zhang, Han Zheng, Nenggan Liu, Chao Yu, Yifei Zhao, Ting Dai, Jianhua |
| AuthorAffiliation | 3 Zhejiang Lab , Hangzhou , China 2 College of Computer Science and Technology, Zhejiang University , Hangzhou , China 4 Collaborative Innovation Center for Artificial Intelligence by MOE and Zhejiang Provincial Government (ZJU) , Hangzhou , China 1 Qiushiq Academy for Advanced Studies (QAAS), Zhejiang University , Hangzhou , China 5 Howard Hughes Medical Institute, Janelia Research Campus , Ashburn, VA , United States |
| AuthorAffiliation_xml | – name: 3 Zhejiang Lab , Hangzhou , China – name: 1 Qiushiq Academy for Advanced Studies (QAAS), Zhejiang University , Hangzhou , China – name: 4 Collaborative Innovation Center for Artificial Intelligence by MOE and Zhejiang Provincial Government (ZJU) , Hangzhou , China – name: 2 College of Computer Science and Technology, Zhejiang University , Hangzhou , China – name: 5 Howard Hughes Medical Institute, Janelia Research Campus , Ashburn, VA , United States |
| Author_xml | – sequence: 1 givenname: Han surname: Zhang fullname: Zhang, Han – sequence: 2 givenname: Chao surname: Liu fullname: Liu, Chao – sequence: 3 givenname: Yifei surname: Yu fullname: Yu, Yifei – sequence: 4 givenname: Jianhua surname: Dai fullname: Dai, Jianhua – sequence: 5 givenname: Ting surname: Zhao fullname: Zhao, Ting – sequence: 6 givenname: Nenggan surname: Zheng fullname: Zheng, Nenggan |
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| Cites_doi | 10.1523/ENEURO.0049-14.2014 10.1007/978-94-015-7744-1_2 10.1016/j.tcs.2004.12.030 10.3389/fnins.2012.00049 10.1109/MCSE.2011.37 10.1007/s12021-011-9117-y 10.1007/s12021-011-9104-3 10.1007/s12021-016-9310-0 10.1038/ncomms12142 10.1016/S0165-0270(98)00091-0 10.1186/1471-2105-13-S8-S7 10.1109/MCSE.2007.58 10.1016/j.neuron.2015.06.036 10.1007/s12021-011-9110-5 10.1016/j.neuron.2013.03.008 10.1038/nbt.1612 10.1007/s12021-011-9120-3 10.1007/s12021-010-9090-x |
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| Copyright | Copyright © 2022 Zhang, Liu, Yu, Dai, Zhao and Zheng. 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2022 Zhang, Liu, Yu, Dai, Zhao and Zheng. 2022 Zhang, Liu, Yu, Dai, Zhao and Zheng |
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| Keywords | toolbox neuron tracing quantitative analysis metric PyNeval neuron reconstruction |
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| References | Peng (B15) 2011; 9 B10 Mayerich (B11) 2012; 13 Cannon (B4) 1998; 84 Gillette (B7); 9 B17 Gong (B8) 2016; 7 Oliphant (B12) 2007; 9 Acciai (B1) 2016; 14 Peng (B14) 2015; 87 Bille (B3) 2005; 337 Parekh (B13) 2013; 77 Halavi (B9) 2012; 6 B2 Wang (B20) 2011; 9 Zhao (B21) 2011; 9 Feng (B5) 2015; 2 Peng (B16) 2010; 28 Van Der Walt (B18) 2011; 13 Gillette (B6); 9 Van Laarhoven (B19) 1987 |
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| SubjectTerms | Algorithms Design metric neuron reconstruction neuron tracing Neuroscience PyNeval Quality control quantitative analysis Software toolbox |
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| Title | PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance |
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