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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Autor*in:	Han Zhang [verfasserIn] Chao Liu [verfasserIn] Yifei Yu [verfasserIn] Jianhua Dai [verfasserIn] Ting Zhao [verfasserIn] Nenggan Zheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	PyNeval metric quantitative analysis neuron tracing neuron reconstruction toolbox

Übergeordnetes Werk:	In: Frontiers in Neuroinformatics - Frontiers Media S.A., 2008, 15(2022)
Übergeordnetes Werk:	volume:15 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fninf.2021.767936

Katalog-ID:	DOAJ047811412

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allfieldsSound	10.3389/fninf.2021.767936 doi (DE-627)DOAJ047811412 (DE-599)DOAJbbbdcb96f20a4b56af2a51e37c6844ef DE-627 ger DE-627 rakwb eng RC321-571 Han Zhang verfasserin aut PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. PyNeval metric quantitative analysis neuron tracing neuron reconstruction toolbox Neurosciences. Biological psychiatry. Neuropsychiatry Han Zhang verfasserin aut Chao Liu verfasserin aut Chao Liu verfasserin aut Yifei Yu verfasserin aut Jianhua Dai verfasserin aut Ting Zhao verfasserin aut Nenggan Zheng verfasserin aut Nenggan Zheng verfasserin aut Nenggan Zheng verfasserin aut In Frontiers in Neuroinformatics Frontiers Media S.A., 2008 15(2022) (DE-627)57982652X (DE-600)2452979-5 16625196 nnns volume:15 year:2022 https://doi.org/10.3389/fninf.2021.767936 kostenfrei https://doaj.org/article/bbbdcb96f20a4b56af2a51e37c6844ef kostenfrei https://www.frontiersin.org/articles/10.3389/fninf.2021.767936/full kostenfrei https://doaj.org/toc/1662-5196 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022
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authorswithroles_txt_mv	Han Zhang @@aut@@ Chao Liu @@aut@@ Yifei Yu @@aut@@ Jianhua Dai @@aut@@ Ting Zhao @@aut@@ Nenggan Zheng @@aut@@
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callnumber-first	R - Medicine
author	Han Zhang
spellingShingle	Han Zhang misc RC321-571 misc PyNeval misc metric misc quantitative analysis misc neuron tracing misc neuron reconstruction misc toolbox misc Neurosciences. Biological psychiatry. Neuropsychiatry PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance
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topic_title	RC321-571 PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance PyNeval metric quantitative analysis neuron tracing neuron reconstruction toolbox
topic	misc RC321-571 misc PyNeval misc metric misc quantitative analysis misc neuron tracing misc neuron reconstruction misc toolbox misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc PyNeval misc metric misc quantitative analysis misc neuron tracing misc neuron reconstruction misc toolbox misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance
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title_auth	PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance
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.
abstractGer	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.
abstract_unstemmed	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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title_short	PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance
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