Retrieving similar substructures on 3D neuron reconstructions

Abstract Since manual tracing is time consuming and the performance of automatic tracing is unstable, it is still a challenging task to generate accurate neuron reconstruction efficiently and effectively. One strategy is generating a reconstruction automatically and then amending its inaccurate part...

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Main Authors: Jian Yang, Yishan He, Xuefeng Liu
Format: Article
Language:English
Published: SpringerOpen 2020-11-01
Series:Brain Informatics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40708-020-00117-x
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spelling doaj-583cb74c150141f7b427e9ef365c5f292020-11-25T04:08:09ZengSpringerOpenBrain Informatics2198-40182198-40262020-11-01711910.1186/s40708-020-00117-xRetrieving similar substructures on 3D neuron reconstructionsJian Yang0Yishan He1Xuefeng Liu2Faculty of Information Technology, Beijing University of TechnologyFaculty of Information Technology, Beijing University of TechnologyFaculty of Information Technology, Beijing University of TechnologyAbstract Since manual tracing is time consuming and the performance of automatic tracing is unstable, it is still a challenging task to generate accurate neuron reconstruction efficiently and effectively. One strategy is generating a reconstruction automatically and then amending its inaccurate parts manually. Aiming at finding inaccurate substructures efficiently, we propose a pipeline to retrieve similar substructures on one or more neuron reconstructions, which are very similar to a marked problematic substructure. The pipeline consists of four steps: getting a marked substructure, constructing a query substructure, generating candidate substructures and retrieving most similar substructures. The retrieval procedure was tested on 163 gold standard reconstructions provided by the BigNeuron project and a reconstruction of a mouse’s large neuron. Experimental results showed that the implementation of the proposed methods is very efficient and all retrieved substructures are very similar to the marked one in numbers of nodes and branches, and degree of curvature.http://link.springer.com/article/10.1186/s40708-020-00117-xNeuronal morphologyReconstructionSubstructureRetrieving
collection DOAJ
language English
format Article
sources DOAJ
author Jian Yang
Yishan He
Xuefeng Liu
spellingShingle Jian Yang
Yishan He
Xuefeng Liu
Retrieving similar substructures on 3D neuron reconstructions
Brain Informatics
Neuronal morphology
Reconstruction
Substructure
Retrieving
author_facet Jian Yang
Yishan He
Xuefeng Liu
author_sort Jian Yang
title Retrieving similar substructures on 3D neuron reconstructions
title_short Retrieving similar substructures on 3D neuron reconstructions
title_full Retrieving similar substructures on 3D neuron reconstructions
title_fullStr Retrieving similar substructures on 3D neuron reconstructions
title_full_unstemmed Retrieving similar substructures on 3D neuron reconstructions
title_sort retrieving similar substructures on 3d neuron reconstructions
publisher SpringerOpen
series Brain Informatics
issn 2198-4018
2198-4026
publishDate 2020-11-01
description Abstract Since manual tracing is time consuming and the performance of automatic tracing is unstable, it is still a challenging task to generate accurate neuron reconstruction efficiently and effectively. One strategy is generating a reconstruction automatically and then amending its inaccurate parts manually. Aiming at finding inaccurate substructures efficiently, we propose a pipeline to retrieve similar substructures on one or more neuron reconstructions, which are very similar to a marked problematic substructure. The pipeline consists of four steps: getting a marked substructure, constructing a query substructure, generating candidate substructures and retrieving most similar substructures. The retrieval procedure was tested on 163 gold standard reconstructions provided by the BigNeuron project and a reconstruction of a mouse’s large neuron. Experimental results showed that the implementation of the proposed methods is very efficient and all retrieved substructures are very similar to the marked one in numbers of nodes and branches, and degree of curvature.
topic Neuronal morphology
Reconstruction
Substructure
Retrieving
url http://link.springer.com/article/10.1186/s40708-020-00117-x
work_keys_str_mv AT jianyang retrievingsimilarsubstructureson3dneuronreconstructions
AT yishanhe retrievingsimilarsubstructureson3dneuronreconstructions
AT xuefengliu retrievingsimilarsubstructureson3dneuronreconstructions
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