Paper Info
Title
<Emphasis Type="Italic">DeepNeuron</Emphasis>: an open deep learning toolbox for neuron tracing
Abstract
Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.
Year
DOI
Venue
2018
10.1186/s40708-018-0081-2
Brain Informatics
Keywords
DocType
Volume
DeepNeuron, Deep learning, Neuron tracing, Neuron morphology
Journal
5
Issue
ISSN
Citations 
2
2198-4026
5
PageRank 
References 
Authors
0.40
10
4
Name
Order
Citations
PageRank
Zhi Zhou152431.51
Hsien-Chi Kuo2151.98
Hanchuan Peng33930182.27
Fuhui Long430419.27