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SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline
BACKGROUND: Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts th...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303825/ https://www.ncbi.nlm.nih.gov/pubmed/37388757 http://dx.doi.org/10.3389/fninf.2023.1174049 |
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author | Ding, Liya Zhao, Xuan Guo, Shuxia Liu, Yufeng Liu, Lijuan Wang, Yimin Peng, Hanchuan |
author_facet | Ding, Liya Zhao, Xuan Guo, Shuxia Liu, Yufeng Liu, Lijuan Wang, Yimin Peng, Hanchuan |
author_sort | Ding, Liya |
collection | PubMed |
description | BACKGROUND: Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons. METHODS: For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting. RESULTS: Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis. |
format | Online Article Text |
id | pubmed-10303825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103038252023-06-29 SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline Ding, Liya Zhao, Xuan Guo, Shuxia Liu, Yufeng Liu, Lijuan Wang, Yimin Peng, Hanchuan Front Neuroinform Neuroscience BACKGROUND: Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons. METHODS: For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting. RESULTS: Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis. Frontiers Media S.A. 2023-06-14 /pmc/articles/PMC10303825/ /pubmed/37388757 http://dx.doi.org/10.3389/fninf.2023.1174049 Text en Copyright © 2023 Ding, Zhao, Guo, Liu, Liu, Wang and Peng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ding, Liya Zhao, Xuan Guo, Shuxia Liu, Yufeng Liu, Lijuan Wang, Yimin Peng, Hanchuan SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline |
title | SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline |
title_full | SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline |
title_fullStr | SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline |
title_full_unstemmed | SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline |
title_short | SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline |
title_sort | snap: a structure-based neuron morphology reconstruction automatic pruning pipeline |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303825/ https://www.ncbi.nlm.nih.gov/pubmed/37388757 http://dx.doi.org/10.3389/fninf.2023.1174049 |
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