Paper Info
Title
DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.
Abstract
Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.
Year
DOI
Venue
2019
10.3389/fninf.2019.00025
FRONTIERS IN NEUROINFORMATICS
Keywords
Field
DocType
DeepBouton,single-neuron,axonal bouton,deep convolutional neural network,density-peak clustering
Pattern recognition,Convolutional neural network,Inference,Computer science,Entire brain,Artificial intelligence,Deep learning,Neurite,Cluster analysis,Neuron,Machine learning
Journal
Volume
ISSN
Citations 
13
1662-5196
1
PageRank 
References 
Authors
0.37
0
12
Name
Order
Citations
PageRank
Shenghua Cheng1111.91
Xiaojun Wang210.37
Yurong Liu31919.40
Lei Su410.37
Tingwei Quan5322.25
Ning Li610.37
Fang-Fang Yin7145.24
Feng Xiong810.37
Xiaomao Liu9433.18
Qingming Luo1014315.71
Hui Gong11368.98
Shaoqun Zeng12101.22