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eNeuro. 2017 Dec 6;4(6). pii: ENEURO.0219-17.2017. doi: 10.1523/ENEURO.0219-17.2017. eCollection 2017 Nov-Dec.

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning.

Author information

1
Child Health Institute of New Jersey, New Brunswick, NJ 08901.
2
Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854.
3
Department of Neuroscience and Cell Biology, Rutgers University, Piscataway, NJ 08854.
4
Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ 08854.
5
Department of Pediatrics, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 08903.

Abstract

Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process.

KEYWORDS:

automated image analysis; high-throughput; immunofluorescence; machine learning; synapse formation; synapse quantification

PMID:
29218324
PMCID:
PMC5718246
DOI:
10.1523/ENEURO.0219-17.2017
[Indexed for MEDLINE]
Free PMC Article

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