Rapid Reconstruction of 3D Neuronal Morphology from Light Microscopy Images with Augmented Rayburst Sampling

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2014 Jan 7

PMID 24391966

Citations 25

Authors

Xing Ming

Anan Li

Jingpeng Wu

Cheng Yan

Wenxiang Ding

Hui Gong

Shaoqun Zeng

Qian Liu

Affiliations

Soon will be listed here.

Abstract

Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.

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References

Ascoli G, Krichmar J, Nasuto S, Senft S . Generation, description and storage of dendritic morphology data. Philos Trans R Soc Lond B Biol Sci. 2001; 356(1412):1131-45. PMC: 1088507. DOI: 10.1098/rstb.2001.0905. View

Wearne S, Rodriguez A, Ehlenberger D, Rocher A, Henderson S, Hof P . New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales. Neuroscience. 2005; 136(3):661-80. DOI: 10.1016/j.neuroscience.2005.05.053. View

Rodriguez A, Ehlenberger D, Kelliher K, Einstein M, Henderson S, Morrison J . Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images. Methods. 2003; 30(1):94-105. DOI: 10.1016/s1046-2023(03)00011-2. View

Yuan X, Trachtenberg J, Potter S, Roysam B . MDL constrained 3-D grayscale skeletonization algorithm for automated extraction of dendrites and spines from fluorescence confocal images. Neuroinformatics. 2009; 7(4):213-32. PMC: 2844542. DOI: 10.1007/s12021-009-9057-y. View

Meijering E, Jacob M, Sarria J, Steiner P, Hirling H, Unser M . Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry A. 2004; 58(2):167-76. DOI: 10.1002/cyto.a.20022. View

Vasilkoski Z, Stepanyants A . Detection of the optimal neuron traces in confocal microscopy images. J Neurosci Methods. 2008; 178(1):197-204. PMC: 2771922. DOI: 10.1016/j.jneumeth.2008.11.008. View

Rodriguez A, Ehlenberger D, Hof P, Wearne S . Three-dimensional neuron tracing by voxel scooping. J Neurosci Methods. 2009; 184(1):169-75. PMC: 2753723. DOI: 10.1016/j.jneumeth.2009.07.021. View

Al-Kofahi Y, Dowell-Mesfin N, Pace C, Shain W, Turner J, Roysam B . Improved detection of branching points in algorithms for automated neuron tracing from 3D confocal images. Cytometry A. 2007; 73(1):36-43. DOI: 10.1002/cyto.a.20499. View

Chothani P, Mehta V, Stepanyants A . Automated tracing of neurites from light microscopy stacks of images. Neuroinformatics. 2011; 9(2-3):263-78. PMC: 3336738. DOI: 10.1007/s12021-011-9121-2. View

10.

Schmitt S, Evers J, Duch C, Scholz M, Obermayer K . New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks. Neuroimage. 2004; 23(4):1283-98. DOI: 10.1016/j.neuroimage.2004.06.047. View

11.

Donohue D, Ascoli G . Automated reconstruction of neuronal morphology: an overview. Brain Res Rev. 2010; 67(1-2):94-102. PMC: 3086984. DOI: 10.1016/j.brainresrev.2010.11.003. View

12.

Al-Kofahi K, Can A, Lasek S, Szarowski D, Dowell-Mesfin N, Shain W . Median-based robust algorithms for tracing neurons from noisy confocal microscope images. IEEE Trans Inf Technol Biomed. 2004; 7(4):302-17. DOI: 10.1109/titb.2003.816564. View

13.

Peng H, Ruan Z, Long F, Simpson J, Myers E . V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat Biotechnol. 2010; 28(4):348-53. PMC: 2857929. DOI: 10.1038/nbt.1612. View

14.

Lu J, Fiala J, Lichtman J . Semi-automated reconstruction of neural processes from large numbers of fluorescence images. PLoS One. 2009; 4(5):e5655. PMC: 2682575. DOI: 10.1371/journal.pone.0005655. View

15.

Li A, Gong H, Zhang B, Wang Q, Yan C, Wu J . Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science. 2010; 330(6009):1404-8. DOI: 10.1126/science.1191776. View

16.

Peng H, Long F, Myers G . Automatic 3D neuron tracing using all-path pruning. Bioinformatics. 2011; 27(13):i239-47. PMC: 3117353. DOI: 10.1093/bioinformatics/btr237. View

17.

Meijering E . Neuron tracing in perspective. Cytometry A. 2010; 77(7):693-704. DOI: 10.1002/cyto.a.20895. View

18.

Gong H, Zeng S, Yan C, Lv X, Yang Z, Xu T . Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution. Neuroimage. 2013; 74:87-98. DOI: 10.1016/j.neuroimage.2013.02.005. View

19.

Turetken E, Gonzalez G, Blum C, Fua P . Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics. 2011; 9(2-3):279-302. DOI: 10.1007/s12021-011-9122-1. View

20.

Roysam B, Shain W, Ascoli G . The central role of neuroinformatics in the National Academy of Engineering's grandest challenge: reverse engineer the brain. Neuroinformatics. 2009; 7(1):1-5. PMC: 2726926. DOI: 10.1007/s12021-008-9043-9. View