Imperial College London
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Vincenzo De Paola

Faculty of Medicine, Department of Brain Sciences

Reader in Translational Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 2501vincenzo.depaola Website CV

 
 
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Assistant

 

Miss Lydia Lawson +44 (0)20 7594 1264

 
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Location

 

Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bass:2017:10.1371/journal.pone.0183309,
author = {Bass, C and Helkkula, P and De, Paola V and Clopath, C and Bharath, AA},
doi = {10.1371/journal.pone.0183309},
journal = {PLoS One},
pages = {1--18},
title = {Detection of axonal synapses in 3D two-photon images},
url = {http://dx.doi.org/10.1371/journal.pone.0183309},
volume = {12},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Studies of structural plasticity in the brain often require the detection and analysis of axonal synapses (boutons). To date, bouton detection has been largely manual or semi-automated, relying on a step that traces the axons before detection the boutons. If tracing the axon fails, the accuracy of bouton detection is compromised. In this paper, we propose a new algorithm that does not require tracing the axon to detect axonal boutons in 3D two-photon images taken from the mouse cortex. To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. The final algorithm proposed has the following main steps: (1) a Laplacian of Gaussian (LoG) based feature enhancement module to accentuate the appearance of boutons; (2) a Speeded Up Robust Features (SURF) interest point detector to find candidate locations for feature extraction; (3) non-maximum suppression to eliminate candidates that were detected more than once in the same local region; (4) generation of feature descriptors based on Gabor filters; (5) a Support Vector Machine (SVM) classifier, trained on features from labelled data, and was used to distinguish between bouton and non-bouton candidates. We found that our method achieved a Recall of 95%, Precision of 76%, and F1 score of 84% within a new dataset that we make available for accessing bouton detection. On average, Recall and F1 score were significantly better than the current state-of-the-art method, while Precision was not significantly different. In conclusion, in this article we demonstrate that our approach, which is independent of axon tracing, can detect boutons to a high level of accuracy, and improves on the detection performance of existing approaches. The data and code (with an easy to use GUI) used in this article are available from open source repositories.
AU  - Bass,C
AU  - Helkkula,P
AU  - De,Paola V
AU  - Clopath,C
AU  - Bharath,AA
DO  - 10.1371/journal.pone.0183309
EP  - 18
PY  - 2017///
SN  - 1932-6203
SP  - 1
TI  - Detection of axonal synapses in 3D two-photon images
T2  - PLoS One
UR  - http://dx.doi.org/10.1371/journal.pone.0183309
UR  - https://www.ncbi.nlm.nih.gov/pubmed/28873436
UR  - https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0183309
UR  - http://hdl.handle.net/10044/1/50520
VL  - 12
ER  -
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