An open-source tool for analysis and automatic identification of dendritic spines using machine learning

Michael S Smirnov, Tavita R Garrett and Ryohei Yasuda

PLOS ONE, 2018, vol. 13, issue 7, 1-16

Abstract: Synaptic plasticity, the cellular basis for learning and memory, is mediated by a complex biochemical network of signaling proteins. These proteins are compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures found on neuronal dendrites. The ability to screen a high number of molecular targets for their effect on dendritic spine structural plasticity will require a high-throughput imaging system capable of stimulating and monitoring hundreds of dendritic spines in various conditions. For this purpose, we present a program capable of automatically identifying dendritic spines in live, fluorescent tissue. Our software relies on a machine learning approach to minimize any need for parameter tuning from the user. Custom thresholding and binarization functions serve to “clean” fluorescent images, and a neural network is trained using features based on the relative shape of the spine perimeter and its corresponding dendritic backbone. Our algorithm is rapid, flexible, has over 90% accuracy in spine detection, and bundled with our user-friendly, open-source, MATLAB-based software package for spine analysis.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0199589

DOI: 10.1371/journal.pone.0199589

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