Tracing weak neuron fibers
Abstract Motivation Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons. Results We propo...
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| Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2023-01, Vol.39 (1) |
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| creator | Liu, Yufeng Zhong, Ye Zhao, Xuan Liu, Lijuan Ding, Liya Peng, Hanchuan |
| description | Abstract
Motivation
Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons.
Results
We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values |
| doi_str_mv | 10.1093/bioinformatics/btac816 |
| format | Article |
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Motivation
Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons.
Results
We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types.
Availability and implementation
Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btac816</identifier><identifier>PMID: 36571479</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Animals ; Availability ; Axons ; Bioinformatics ; Brain ; Circuits ; Fibers ; Image resolution ; Mice ; Neurites ; Neuroimaging ; Neurons ; Reconstruction ; Software ; Tracing</subject><ispartof>Bioinformatics (Oxford, England), 2023-01, Vol.39 (1)</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-f9ed4e4ba140c086a2ca11bc7f3354fef6dd1481a3cf316c10f3e106d74fec183</citedby><cites>FETCH-LOGICAL-c429t-f9ed4e4ba140c086a2ca11bc7f3354fef6dd1481a3cf316c10f3e106d74fec183</cites><orcidid>0000-0003-0848-5113 ; 0000-0002-1209-875X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36571479$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Yufeng</creatorcontrib><creatorcontrib>Zhong, Ye</creatorcontrib><creatorcontrib>Zhao, Xuan</creatorcontrib><creatorcontrib>Liu, Lijuan</creatorcontrib><creatorcontrib>Ding, Liya</creatorcontrib><creatorcontrib>Peng, Hanchuan</creatorcontrib><title>Tracing weak neuron fibers</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Abstract
Motivation
Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons.
Results
We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types.
Availability and implementation
Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25.
Supplementary information
Supplementary data are available at Bioinformatics online.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Availability</subject><subject>Axons</subject><subject>Bioinformatics</subject><subject>Brain</subject><subject>Circuits</subject><subject>Fibers</subject><subject>Image resolution</subject><subject>Mice</subject><subject>Neurites</subject><subject>Neuroimaging</subject><subject>Neurons</subject><subject>Reconstruction</subject><subject>Software</subject><subject>Tracing</subject><issn>1367-4803</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNqNkE9LxDAQxYMo7rr6BTwsC1681M100rQ9yuI_WPCynkOaJpK1bdakRfz2Rrou6MnTDMzvvXk8QuZAb4CWuKyss51xvpW9VWFZ9VIVwI_IFJDnCSsAjg87xQk5C2FLKc1oxk_JBHmWA8vLKbnceKls97r40PJt0enBu25hbKV9OCcnRjZBX-znjLzc321Wj8n6-eFpdbtOFEvLPjGlrplmlQRGFS24TJUEqFRuEDNmtOF1DTGQRGUQuAJqUAPldR6PCgqckevRd-fd-6BDL1oblG4a2Wk3BJHmWYEZRwYRvfqDbt3gu5hOIMR3KbIIzggfKeVdCF4bsfO2lf5TABXf7Ynf7Yl9e1E439sPVavrg-ynrgjACLhh91_TL0o5gMM</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Liu, Yufeng</creator><creator>Zhong, Ye</creator><creator>Zhao, Xuan</creator><creator>Liu, Lijuan</creator><creator>Ding, Liya</creator><creator>Peng, Hanchuan</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7TO</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0848-5113</orcidid><orcidid>https://orcid.org/0000-0002-1209-875X</orcidid></search><sort><creationdate>20230101</creationdate><title>Tracing weak neuron fibers</title><author>Liu, Yufeng ; Zhong, Ye ; Zhao, Xuan ; Liu, Lijuan ; Ding, Liya ; Peng, Hanchuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c429t-f9ed4e4ba140c086a2ca11bc7f3354fef6dd1481a3cf316c10f3e106d74fec183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Availability</topic><topic>Axons</topic><topic>Bioinformatics</topic><topic>Brain</topic><topic>Circuits</topic><topic>Fibers</topic><topic>Image resolution</topic><topic>Mice</topic><topic>Neurites</topic><topic>Neuroimaging</topic><topic>Neurons</topic><topic>Reconstruction</topic><topic>Software</topic><topic>Tracing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yufeng</creatorcontrib><creatorcontrib>Zhong, Ye</creatorcontrib><creatorcontrib>Zhao, Xuan</creatorcontrib><creatorcontrib>Liu, Lijuan</creatorcontrib><creatorcontrib>Ding, Liya</creatorcontrib><creatorcontrib>Peng, Hanchuan</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yufeng</au><au>Zhong, Ye</au><au>Zhao, Xuan</au><au>Liu, Lijuan</au><au>Ding, Liya</au><au>Peng, Hanchuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tracing weak neuron fibers</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2023-01-01</date><risdate>2023</risdate><volume>39</volume><issue>1</issue><issn>1367-4803</issn><eissn>1367-4811</eissn><abstract>Abstract
Motivation
Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons.
Results
We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types.
Availability and implementation
Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25.
Supplementary information
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36571479</pmid><doi>10.1093/bioinformatics/btac816</doi><orcidid>https://orcid.org/0000-0003-0848-5113</orcidid><orcidid>https://orcid.org/0000-0002-1209-875X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Animals Availability Axons Bioinformatics Brain Circuits Fibers Image resolution Mice Neurites Neuroimaging Neurons Reconstruction Software Tracing |
| title | Tracing weak neuron fibers |
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