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Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

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Abstract

The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from hundreds of microns to hundreds of nanometers. In this paper, we address these challenges and present a computational framework for an automatic, three-dimensional (3D) morphological reconstruction of live nerve cells. The key aspects of this approach are: (i) detection of neuronal dendrites through learning 3D tubular models, and (ii) skeletonization by a new algorithm using a morphology-guided deformable model for extracting the dendritic centerline. To represent the neuron morphology, we introduce a novel representation, the Minimum Shape-Cost (MSC) Tree that approximates the dendrite centerline with sub-voxel accuracy and demonstrate the uniqueness of such a shape representation as well as its computational efficiency. We present extensive quantitative and qualitative results that demonstrate the accuracy and robustness of our method.

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Notes

  1. Loops are not allowed in a tree structure.

  2. The coordinates of the points in the soma-pipette region are used to obtain the center of the ellipse given by the mean of the points and the semi-axis of the ellipse given by the covariance matrix.

  3. The solution of Eq. (4) coincides with the centerline of each dendritic branch since small distances of a voxel fromVD are penalized and the only points that are equidistant from the dendritic boundary are those of the centerline.

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Acknowledgments

We wish to thank all of the members of the ORION team (Computational Biomedicine Lab2014) and especially Costa M. Colbert, Yong Liang, and Bradley E. Losavio. The data were acquired at P. Saggau’s Laboratory in the Department of Neuroscience of the Baylor College of Medicine. This work was supported in part by NIH 5R01EB001048-02, NSF-DMS 0915242, NHARP 003652-0136-2009 and the University of Houston–Eckhard Pfeiffer Endowment Fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and may not reflect the views of UH, NIH, NHARP, or NSF.

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Authors and Affiliations

  1. Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, 77204, USA

    Alberto Santamaría-Pang, Paul Hernandez-Herrera & Manos Papadakis

  2. Department of Mathematics, University of Houston, Houston, TX, 77204, USA

    Peter Saggau

  3. Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA

    Ioannis A. Kakadiaris

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  1. Alberto Santamaría-Pang

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  2. Paul Hernandez-Herrera

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  3. Manos Papadakis

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  4. Peter Saggau

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  5. Ioannis A. Kakadiaris

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Correspondence toIoannis A. Kakadiaris.

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Santamaría-Pang, A., Hernandez-Herrera, P., Papadakis, M.et al. Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models.Neuroinform13, 297–320 (2015). https://doi.org/10.1007/s12021-014-9253-2

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