A neuron image segmentation method based Deep Boltzmann Machine and CV model.

He, F; Huang, X; Wang, X; Qiu, S; Jiang, F; Ling, SH

A neuron image segmentation method based Deep Boltzmann Machine and CV model.

He, F Huang, X Wang, X Qiu, S Jiang, F

Ling, SH

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021, 89, pp. 101871
Issue Date:: 2021-04

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Field	Value	Language
dc.contributor.author	He, F
dc.contributor.author	Huang, X
dc.contributor.author	Wang, X
dc.contributor.author	Qiu, S
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.contributor.author	Ling, SH
dc.date.accessioned	2022-01-04T00:35:07Z
dc.date.available	2021-01-21
dc.date.available	2022-01-04T00:35:07Z
dc.date.issued	2021-04
dc.identifier.citation	Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021, 89, pp. 101871
dc.identifier.issn	0895-6111
dc.identifier.issn	1879-0771
dc.identifier.uri	http://hdl.handle.net/10453/152624
dc.description.abstract	Neuron image segmentation has wide applications and important potential values for neuroscience research. Due to the complexity of the submicroscopic structure of neurons cells and the defects of the image quality such as anisotropy, boundary loss and blurriness in electron microscopy-based (EM) imaging, and one faces a challenge in efficient automated segmenting large-scale neuron image 3D datasets, which is an essential prerequisite front-end process for the reconstruction of neuron circuits. Here, a neuron image segmentation method by combining Chan-Vest (CV) model with Deep Boltzmann Machine (DBM) is proposed, and a generative model is used to model and generate the target shape, it take this as a prior information to add global target shape feature constraint to the energy function of CV model, and the shape priori information is fused to assist neuron image segmentation. We applied our method to two 3D-EM datasets from different types of nerve tissue and achieved the best performance consistently across two classical evaluation metrics of neuron segmentation accuracy, namely Variation of Information (VoI) and Adaptive Rand Index (ARI). Experimental results show that the fusion algorithm has high segmentation accuracy, strong robustness, and can characterize the sub-microstructure information of neuron images well.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
dc.relation.isbasedon	10.1016/j.compmedimag.2021.101871
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering, 1103 Clinical Sciences
dc.subject.classification	Nuclear Medicine & Medical Imaging
dc.subject.mesh	Neurons
dc.subject.mesh	Microscopy, Electron
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Microscopy, Electron
dc.subject.mesh	Neurons
dc.title	A neuron image segmentation method based Deep Boltzmann Machine and CV model.
dc.type	Journal Article
utslib.citation.volume	89
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	1103 Clinical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-04T00:35:03Z
pubs.publication-status	Published
pubs.volume	89

Abstract:

Neuron image segmentation has wide applications and important potential values for neuroscience research. Due to the complexity of the submicroscopic structure of neurons cells and the defects of the image quality such as anisotropy, boundary loss and blurriness in electron microscopy-based (EM) imaging, and one faces a challenge in efficient automated segmenting large-scale neuron image 3D datasets, which is an essential prerequisite front-end process for the reconstruction of neuron circuits. Here, a neuron image segmentation method by combining Chan-Vest (CV) model with Deep Boltzmann Machine (DBM) is proposed, and a generative model is used to model and generate the target shape, it take this as a prior information to add global target shape feature constraint to the energy function of CV model, and the shape priori information is fused to assist neuron image segmentation. We applied our method to two 3D-EM datasets from different types of nerve tissue and achieved the best performance consistently across two classical evaluation metrics of neuron segmentation accuracy, namely Variation of Information (VoI) and Adaptive Rand Index (ARI). Experimental results show that the fusion algorithm has high segmentation accuracy, strong robustness, and can characterize the sub-microstructure information of neuron images well.

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http://hdl.handle.net/10453/152624