ArticleNeuroinformatics2017
Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks.
Article in Neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- DeepD3, an open framework for automated quantification of dendritic spines.PLoS computational biology · 2024Article
- Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders.Frontiers in neuroscience · 2024Review
- Current Best Practices for Analysis of Dendritic Spine Morphology and Number in Neurodevelopmental Disorder Research.ACS chemical neuroscience · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures, current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging, where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge, we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging. This framework includes: (i) a novel preprocessing algorithm enhancing spines in a way that they are included in the binarized volume produced during the segmentation of foreground from background; (ii) the mathematical foundation of this algorithm, and (iii) an algorithm for the detection of spine locations in reference to centerline trace and separating them from the branches to whom spines are attached to. This framework enables the computation of a wide range of geometric features such as spine length, spatial distribution and spine volume in a high-throughput fashion. We illustrate our approach for the automated extraction of dendritic spine features in time-series multi-photon images of layer 5 cortical excitatory neurons from the mouse visual cortex.
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Registered trials
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