MorphoNeuroNet: An Automated Method for Dense Neurite Network Analysis

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MorphoNeuroNet: An Automated Method for Dense Neurite Network Analysis. / Pani, Giuseppe; De Vos, Winnok H.; Samari, Nada; de Saint-Georges, Louis; Baatout, Sarah; Van Oostveldt, Patrick; Benotmane, Rafi; Quintens, Roel (Peer reviewer).

In: Cytometry part A, Vol. 85, No. 2, 02.2014, p. 188-199.

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@article{ab838e96944d40739c3fbff7daae8d71,
title = "MorphoNeuroNet: An Automated Method for Dense Neurite Network Analysis",
abstract = "High content cell-based screens are rapidly gaining popularity in the context of neuronal regeneration studies. To analyze neuronal morphology, automatic image analysis pipelines have been conceived, which accurately quantify the shape changes of neurons in cell cultures with non-dense neurite networks. Here, we present a fully automated method for quantifying the morphology of neurons and the density of neurite networks, in dense neuronal cultures, which are grown for more than 10 days.MorphoNeuroNet, written as a script for ImageJ, Java based freeware, automatically determines various morphological parameters of the soma and the neurites (size, shape, starting points, and fractional occupation). The image analysis pipeline consists of a multi-tier approach in which the somas are segmented by adaptive region growing using nuclei as seeds, and the neurites are delineated by a combination of various intensity and edge detection algorithms. Quantitative comparison showed a superior performance of MorphoNeuroNet to existing analysis tools, especially for revealing subtle changes in thin neurites, which have weak fluorescence intensity compared to the rest of the network. The proposed method will help determining the effects of compounds on cultures with dense neurite networks, thereby boosting physiological relevance of cell-based assays in the context of neuronal diseases.",
keywords = "mature neuronal network, neurite tracing, neuronal morphology, image analysis, ImageJ",
author = "Giuseppe Pani and {De Vos}, {Winnok H.} and Nada Samari and {de Saint-Georges}, Louis and Sarah Baatout and {Van Oostveldt}, Patrick and Rafi Benotmane and Roel Quintens",
note = "Score = 10",
year = "2014",
month = "2",
doi = "10.1002/cyto.a.22408",
language = "English",
volume = "85",
pages = "188--199",
journal = "Cytometry part A",
issn = "1552-4922",
publisher = "Wiley - John Wiley & Sons, Ltd",
number = "2",

}

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TY - JOUR

T1 - MorphoNeuroNet: An Automated Method for Dense Neurite Network Analysis

AU - Pani, Giuseppe

AU - De Vos, Winnok H.

AU - Samari, Nada

AU - de Saint-Georges, Louis

AU - Baatout, Sarah

AU - Van Oostveldt, Patrick

AU - Benotmane, Rafi

A2 - Quintens, Roel

N1 - Score = 10

PY - 2014/2

Y1 - 2014/2

N2 - High content cell-based screens are rapidly gaining popularity in the context of neuronal regeneration studies. To analyze neuronal morphology, automatic image analysis pipelines have been conceived, which accurately quantify the shape changes of neurons in cell cultures with non-dense neurite networks. Here, we present a fully automated method for quantifying the morphology of neurons and the density of neurite networks, in dense neuronal cultures, which are grown for more than 10 days.MorphoNeuroNet, written as a script for ImageJ, Java based freeware, automatically determines various morphological parameters of the soma and the neurites (size, shape, starting points, and fractional occupation). The image analysis pipeline consists of a multi-tier approach in which the somas are segmented by adaptive region growing using nuclei as seeds, and the neurites are delineated by a combination of various intensity and edge detection algorithms. Quantitative comparison showed a superior performance of MorphoNeuroNet to existing analysis tools, especially for revealing subtle changes in thin neurites, which have weak fluorescence intensity compared to the rest of the network. The proposed method will help determining the effects of compounds on cultures with dense neurite networks, thereby boosting physiological relevance of cell-based assays in the context of neuronal diseases.

AB - High content cell-based screens are rapidly gaining popularity in the context of neuronal regeneration studies. To analyze neuronal morphology, automatic image analysis pipelines have been conceived, which accurately quantify the shape changes of neurons in cell cultures with non-dense neurite networks. Here, we present a fully automated method for quantifying the morphology of neurons and the density of neurite networks, in dense neuronal cultures, which are grown for more than 10 days.MorphoNeuroNet, written as a script for ImageJ, Java based freeware, automatically determines various morphological parameters of the soma and the neurites (size, shape, starting points, and fractional occupation). The image analysis pipeline consists of a multi-tier approach in which the somas are segmented by adaptive region growing using nuclei as seeds, and the neurites are delineated by a combination of various intensity and edge detection algorithms. Quantitative comparison showed a superior performance of MorphoNeuroNet to existing analysis tools, especially for revealing subtle changes in thin neurites, which have weak fluorescence intensity compared to the rest of the network. The proposed method will help determining the effects of compounds on cultures with dense neurite networks, thereby boosting physiological relevance of cell-based assays in the context of neuronal diseases.

KW - mature neuronal network

KW - neurite tracing

KW - neuronal morphology

KW - image analysis

KW - ImageJ

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_134141

UR - http://knowledgecentre.sckcen.be/so2/bibref/11229

U2 - 10.1002/cyto.a.22408

DO - 10.1002/cyto.a.22408

M3 - Article

VL - 85

SP - 188

EP - 199

JO - Cytometry part A

JF - Cytometry part A

SN - 1552-4922

IS - 2

ER -

ID: 374152