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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Acknowledgments
The authors want to thank Professor Tara Keck of the MRC Centre for Developmental Neurobiology, King’s College, London, UK and Dr. Mari Sajo, of the Icahn School of Medicine at Mount Sinai for providing us with data sets and guidance for the experimental verification of our method. Without their help this work would never have come to realization. Many thanks to Dr. Ryan Ash, MD, of the Baylor College of Medicine for this help with manual validations and Neurolucida 360 and for sharing with us his insight on spines. Also many thanks have to go to our doctoral student Mr. Nikolaos Karantzas for helping us with the validation experiments. Finally, we thank Professor Ioannis Kakadiaris of the University of Houston for several helpful discussions on 3-D volume segmentation.
This work was partially supported by NSF-DMS 1320910 and by a GEAR 2015 grant awarded by the Division of Research of the University of Houston and by a CONACYT graduate scholarship. Last, but, by no means least, we express our warm thanks to the reviewers who gave us insightful comments and suggestions to improve the original manuscript.
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Singh, P.K., Hernandez-Herrera, P., Labate, D. et al. Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks. Neuroinform 15, 303–319 (2017). https://doi.org/10.1007/s12021-017-9332-2
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DOI: https://doi.org/10.1007/s12021-017-9332-2