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Detection of axonal synapses in 3D two-photon images
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 9, p e0183309 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
Male
Computer science
Physiology
FEATURES
SEGMENTATION
lcsh:Medicine
Nervous System
Polynomials
Synapse
Machine Learning
0302 clinical medicine
Nerve Fibers
Animal Cells
Medicine and Health Sciences
Segmentation
Axon
lcsh:Science
Neurons
Multidisciplinary
Applied Mathematics
Simulation and Modeling
DENDRITIC SPINES
Multidisciplinary Sciences
Electrophysiology
Veterinary Surgery
medicine.anatomical_structure
FILTER
Databases as Topic
Physical Sciences
Science & Technology - Other Topics
Cellular Types
Anatomy
F1 score
Algorithms
Research Article
Veterinary Medicine
Computer and Information Sciences
General Science & Technology
Imaging Techniques
Feature extraction
STRUCTURAL DYNAMICS
Presynaptic Terminals
Neurophysiology
Feature selection
Blob detection
Research and Analysis Methods
03 medical and health sciences
Imaging, Three-Dimensional
Artificial Intelligence
Support Vector Machines
medicine
Animals
RECONSTRUCTION
Science & Technology
Mouse cortex
business.industry
CELL-TYPE
lcsh:R
Biology and Life Sciences
Pattern recognition
Cell Biology
BOUTONS
Axons
Support vector machine
Interest point detection
Mice, Inbred C57BL
030104 developmental biology
Algebra
Microscopy, Fluorescence, Multiphoton
Cellular Neuroscience
Structural plasticity
Synapses
FEATURE-SELECTION
lcsh:Q
Veterinary Science
Artificial intelligence
business
030217 neurology & neurosurgery
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....6e45eabdff6561411905b5c88ee0f967