NeuroQuantify – An image analysis software for detection and quantification of 
neuron cells and neurite lengths using deep learning

Dang, Ka My; Zhang, Yi Jia; Zhang, Tianchen; Wang, Chao; Sinner, Anton; Coronica, Piero; Poon, Joyce K. S.

doi:10.1016/j.jneumeth.2024.110273

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

NeuroQuantify – An image analysis software for detection and quantification of neuron cells and neurite lengths using deep learning

MPS-Authors

/persons/resource/persons286700

Dang, Ka My
Nanophotonics, Integration, and Neural Technology, Max Planck Institute of Microstructure Physics, Max Planck Society;
Max Planck - University of Toronto Centre for Neural Science and Technology, Max Planck Institute of Microstructure Physics, Max Planck Society;

/persons/resource/persons301943

Sinner, Anton
Nanophotonics, Integration, and Neural Technology, Max Planck Institute of Microstructure Physics, Max Planck Society;

/persons/resource/persons257612

Poon, Joyce K. S.
Nanophotonics, Integration, and Neural Technology, Max Planck Institute of Microstructure Physics, Max Planck Society;
Max Planck - University of Toronto Centre for Neural Science and Technology, Max Planck Institute of Microstructure Physics, Max Planck Society;

External Resource

https://doi.org/10.1016/j.jneumeth.2024.110273
(Publisher version)

https://github.com/StanleyZ0528/neural-image-segmentation
(Code)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

1-s2.0-S0165027024002188-main.pdf
(Publisher version), 6MB

Supplementary Material (public)

There is no public supplementary material available

Citation

Dang, K. M., Zhang, Y. J., Zhang, T., Wang, C., Sinner, A., Coronica, P., et al. (2024). NeuroQuantify – An image analysis software for detection and quantification of neuron cells and neurite lengths using deep learning. Journal of Neuroscience Methods, 411: 110273. doi:10.1016/j.jneumeth.2024.110273.

Cite as: https://hdl.handle.net/21.11116/0000-000F-DEB7-9

Abstract

Background: The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals.

New method: We have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images.

Results: NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations.

Comparison with existing methods: NeuroQuantify overcomes some of the limitations of existing methods in the automatic and accurate analysis of neuronal structures. It has been developed for phase contrast images rather than fluorescence images. In addition to typical functionality of cell counting, NeuroQuantify also detects and counts neurites, measures the neurite lengths, and produces the neurite orientation distribution.

Conclusions: We offer a valuable tool to assess network development rapidly and effectively. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub at https://github.com/StanleyZ0528/neural-image-segmentation