Automated Curation of CNMF-E-Extracted ROI Spatial Footprints and Calcium Traces Using Open-Source AutoML Tools
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- @Article{tran:2020:fncir,
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author = "Lina M. Tran and Andrew J. Mocle and
Adam I. Ramsaran and Alexander D. Jacob and Paul W. Frankland and
Sheena A. Josselyn",
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title = "Automated Curation of {CNMF-E-Extracted ROI} Spatial
Footprints and Calcium Traces Using Open-Source
{AutoML} Tools",
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journal = "Frontiers in Neural Circuits",
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year = "2020",
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volume = "14",
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number = "42",
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month = jul # " 15",
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keywords = "genetic algorithms, genetic programming, TPOT,
AutoSklearn, Python, calcium imaging, open-source,
machine learning, microendoscopy, image processing,
ROI, region of interest",
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ISSN = "1662-5110",
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code_url = "https://github.com/jf-lab/cnmfe-reviewer",
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URL = "https://www.biorxiv.org/content/10.1101/2020.03.13.991216v1",
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DOI = "doi:10.3389/fncir.2020.00042",
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abstract = "In vivo 1-photon (1p) calcium imaging is an
increasingly prevalent method in behavioral
neuroscience. Numerous analysis pipelines have been
developed to improve the reliability and scalability of
pre-processing and ROI extraction for these large
calcium imaging datasets. Despite these advancements in
pre-processing methods, manual curation of the
extracted spatial footprints and calcium traces of
neurons remains important for quality control. Here, we
propose an additional semi-automated curation step for
sorting spatial footprints and calcium traces from
putative neurons extracted using the popular
constrained non-negative matrix factorization for
microendoscopic data (CNMF-E) algorithm. We used the
automated machine learning (AutoML) tools TPOT and
AutoSklearn to generate classifiers to curate the
extracted ROIs trained on a subset of human-labeled
data. AutoSklearn produced the best performing
classifier, achieving an F1 score greater than 92
percent on the ground truth test dataset. This
automated approach is a useful strategy for filtering
ROIs with relatively few labeled data points and can be
easily added to pre-existing pipelines currently using
CNMF-E for ROI extraction.",
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notes = "Hospital for Sick Children, Neurosciences and Mental
Health, Toronto, ON, Canada
The datasets and code generated for this study can be
found in the cnmfe-reviewer GitHub repository
(https://github.com/jf-lab/cnmfe-reviewer).
PMID: 32792911; PMCID: PMC7384547",
- }
Genetic Programming entries for
Lina M Tran
Andrew J Mocle
Adam I Ramsaran
Alexander D Jacob
Paul W Frankland
Sheena A Josselyn
Citations