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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2020-11-01
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Online Access: | http://link.springer.com/article/10.1186/s40708-020-00117-x |
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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 |
_version_ |
1724426524177727488 |