Classifying the tracing difficulty of 3D neuron image blocks based on deep learning

Abstract Quickly and accurately tracing neuronal morphologies in large-scale volumetric microscopy data is a very challenging task. Most automatic algorithms for tracing multi-neuron in a whole brain are designed under the Ultra-Tracer framework, which begins the tracing of a neuron from its soma an...

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Autores principales:	Bin Yang, Jiajin Huang, Gaowei Wu, Jian Yang
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Deep learning Tracing difficulty classification Residual neural network Fully connected neural network Long short-term memory network Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/a0338b83def94a45bd3b5e560e5ee50c
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spelling	oai:doaj.org-article:a0338b83def94a45bd3b5e560e5ee50c2021-11-07T12:11:52ZClassifying the tracing difficulty of 3D neuron image blocks based on deep learning10.1186/s40708-021-00146-02198-40182198-4026https://doaj.org/article/a0338b83def94a45bd3b5e560e5ee50c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s40708-021-00146-0https://doaj.org/toc/2198-4018https://doaj.org/toc/2198-4026Abstract Quickly and accurately tracing neuronal morphologies in large-scale volumetric microscopy data is a very challenging task. Most automatic algorithms for tracing multi-neuron in a whole brain are designed under the Ultra-Tracer framework, which begins the tracing of a neuron from its soma and traces all signals via a block-by-block strategy. Some neuron image blocks are easy for tracing and their automatic reconstructions are very accurate, and some others are difficult and their automatic reconstructions are inaccurate or incomplete. The former are called low Tracing Difficulty Blocks (low-TDBs), while the latter are called high Tracing Difficulty Blocks (high-TDBs). We design a model named 3D-SSM to classify the tracing difficulty of 3D neuron image blocks, which is based on 3D Residual neural Network (3D-ResNet), Fully Connected Neural Network (FCNN) and Long Short-Term Memory network (LSTM). 3D-SSM contains three modules: Structure Feature Extraction (SFE), Sequence Information Extraction (SIE) and Model Fusion (MF). SFE utilizes a 3D-ResNet and a FCNN to extract two kinds of features in 3D image blocks and their corresponding automatic reconstruction blocks. SIE uses two LSTMs to learn sequence information hidden in 3D image blocks. MF adopts a concatenation operation and a FCNN to combine outputs from SIE. 3D-SSM can be used as a stop condition of an automatic tracing algorithm in the Ultra-Tracer framework. With its help, neuronal signals in low-TDBs can be traced by the automatic algorithm and in high-TDBs may be reconstructed by annotators. 12732 training samples and 5342 test samples are constructed on neuron images of a whole mouse brain. The 3D-SSM achieves classification accuracy rates 87.04% on the training set and 84.07% on the test set. Furthermore, the trained 3D-SSM is tested on samples from another whole mouse brain and its accuracy rate is 83.21%.Bin YangJiajin HuangGaowei WuJian YangSpringerOpenarticleDeep learningTracing difficulty classificationResidual neural networkFully connected neural networkLong short-term memory networkComputer applications to medicine. Medical informaticsR858-859.7Computer softwareQA76.75-76.765ENBrain Informatics, Vol 8, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning Tracing difficulty classification Residual neural network Fully connected neural network Long short-term memory network Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
spellingShingle	Deep learning Tracing difficulty classification Residual neural network Fully connected neural network Long short-term memory network Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765 Bin Yang Jiajin Huang Gaowei Wu Jian Yang Classifying the tracing difficulty of 3D neuron image blocks based on deep learning
description	Abstract Quickly and accurately tracing neuronal morphologies in large-scale volumetric microscopy data is a very challenging task. Most automatic algorithms for tracing multi-neuron in a whole brain are designed under the Ultra-Tracer framework, which begins the tracing of a neuron from its soma and traces all signals via a block-by-block strategy. Some neuron image blocks are easy for tracing and their automatic reconstructions are very accurate, and some others are difficult and their automatic reconstructions are inaccurate or incomplete. The former are called low Tracing Difficulty Blocks (low-TDBs), while the latter are called high Tracing Difficulty Blocks (high-TDBs). We design a model named 3D-SSM to classify the tracing difficulty of 3D neuron image blocks, which is based on 3D Residual neural Network (3D-ResNet), Fully Connected Neural Network (FCNN) and Long Short-Term Memory network (LSTM). 3D-SSM contains three modules: Structure Feature Extraction (SFE), Sequence Information Extraction (SIE) and Model Fusion (MF). SFE utilizes a 3D-ResNet and a FCNN to extract two kinds of features in 3D image blocks and their corresponding automatic reconstruction blocks. SIE uses two LSTMs to learn sequence information hidden in 3D image blocks. MF adopts a concatenation operation and a FCNN to combine outputs from SIE. 3D-SSM can be used as a stop condition of an automatic tracing algorithm in the Ultra-Tracer framework. With its help, neuronal signals in low-TDBs can be traced by the automatic algorithm and in high-TDBs may be reconstructed by annotators. 12732 training samples and 5342 test samples are constructed on neuron images of a whole mouse brain. The 3D-SSM achieves classification accuracy rates 87.04% on the training set and 84.07% on the test set. Furthermore, the trained 3D-SSM is tested on samples from another whole mouse brain and its accuracy rate is 83.21%.
format	article
author	Bin Yang Jiajin Huang Gaowei Wu Jian Yang
author_facet	Bin Yang Jiajin Huang Gaowei Wu Jian Yang
author_sort	Bin Yang
title	Classifying the tracing difficulty of 3D neuron image blocks based on deep learning
title_short	Classifying the tracing difficulty of 3D neuron image blocks based on deep learning
title_full	Classifying the tracing difficulty of 3D neuron image blocks based on deep learning
title_fullStr	Classifying the tracing difficulty of 3D neuron image blocks based on deep learning
title_full_unstemmed	Classifying the tracing difficulty of 3D neuron image blocks based on deep learning
title_sort	classifying the tracing difficulty of 3d neuron image blocks based on deep learning
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/a0338b83def94a45bd3b5e560e5ee50c
work_keys_str_mv	AT binyang classifyingthetracingdifficultyof3dneuronimageblocksbasedondeeplearning AT jiajinhuang classifyingthetracingdifficultyof3dneuronimageblocksbasedondeeplearning AT gaoweiwu classifyingthetracingdifficultyof3dneuronimageblocksbasedondeeplearning AT jianyang classifyingthetracingdifficultyof3dneuronimageblocksbasedondeeplearning
_version_	1718443506115018752

Classifying the tracing difficulty of 3D neuron image blocks based on deep learning

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