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Improved Automatic Centerline Tracing for Dendritic and Axonal Structures

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Abstract

Centerline tracing in dendritic structures acquired from confocal images of neurons is an essential tool for the construction of geometrical representations of a neuronal network from its coarse scale up to its fine scale structures. In this paper, we propose an algorithm for centerline extraction that is both highly accurate and computationally efficient. The main novelties of the proposed method are (1) the use of a small set of Multiscale Isotropic Laplacian filters, acting as self-steerable filters, for a quick and efficient binary segmentation of dendritic arbors and axons; (2) an automated centerline seed points detection method based on the application of a simple 3D finite-length filter. The performance of this algorithm, which is validated on data from the DIADEM set appears to be very competitive when compared with other state-of-the-art algorithms.

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Notes

  1. The roof over the symbol of the filter denotes its Fourier transform.

  2. These properties imply that the filter ϕ and all of its derivatives up to second order are well-localized in space. This means that, for practical purposes, the spatial support of ϕ and of its derivatives up to second order is small.

  3. This quantity represents that ratio of the length in the z-direction of a voxel relative to its length in the x,y directions. Therefore, it characterizes the sampling grid and thus it shows the anisotropy of the point-spread function in the z-direction.

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Acknowledgments

This work was supported in part by NSF-DMS 1320910, 0915242, 1008900, 1005799 and by NHARP-003652-0136-2009. I.A. Kakadiaris was also supported by the Hugh Roy and Lillie Cranz Cullen Endowment Fund. We thank our student P. Hernandez-Herrera for allowing us to use the performance test results he obtained with Neuronstudio, ORION and APP2 on numerous data sets. Last, but most certainly not least, we would like to thank the three reviewers for their encouraging comments and their valuable input which helped us improve the quality of the paper.

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Costa Rica, San Pedro Montes de Oca, San José, Republic of Costa Rica

    David Jiménez

  2. Department of Mathematics, University of Houston, 641 Phillip G Hoffman Hall, Houston, TX, 772004-3008, USA

    Demetrio Labate & Manos Papadakis

  3. Computational Biomedicine Lab, Department of Computer Science, University of Houston, 4800 Calhoun Rd, Houston, TX, 77204, USA

    Ioannis A. Kakadiaris

Authors
  1. David Jiménez
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  2. Demetrio Labate
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  3. Ioannis A. Kakadiaris
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  4. Manos Papadakis
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Corresponding author

Correspondence to Manos Papadakis.

Additional information

Information Sharing Statement

Matlab source code of all binary segmentation, seeding and tracing routines are publicly available for download at http://www.math.uh.edu/~mpapadak/centerline

A manual is included in the same folder together with the source code. The code works for 3-D data sets, but it can be modified for use with 2-D sets. Please cite the above web address if you use our code for any publication or commercial purpose. All data sets used in our experiments are publicly available at the DIADEM competition (RRID:nif-0000-23194) website.

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Jiménez, D., Labate, D., Kakadiaris, I.A. et al. Improved Automatic Centerline Tracing for Dendritic and Axonal Structures. Neuroinform 13, 227–244 (2015). https://doi.org/10.1007/s12021-014-9256-z

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