Automated reconstruction of neuronal morphology: An overview

Digital reconstruction of neuronal morphology is a powerful technique for investigating the nervous system. This process consists of tracing the axonal and dendritic arbors of neurons imaged by optical microscopy into a geometrical format suitable for quantitative analysis and computational modeling. Algorithmic automation of neuronal tracing promises to increase the speed, accuracy, and reproducibility of morphological reconstructions. Together with recent breakthroughs in cellular imaging and accelerating progress in optical microscopy, automated reconstruction of neuronal morphology will play a central role in the development of high throughput screening and the acquisition of connectomic data. Yet, despite continuous advances in image processing algorithms, to date manual tracing remains the overwhelming choice for digitizing neuronal morphology. We summarize the issues involved in automated reconstruction, overview the available techniques, and provide a realistic assessment of future perspectives.

[1]  Michael L. Hines,et al.  ModelDB - Making models publicly accessible to support computational neuroscience , 2003, Neuroinformatics.

[2]  G. Ascoli,et al.  Quantitative morphometry of hippocampal pyramidal cells: Differences between anatomical classes and reconstructing laboratories , 2004, The Journal of comparative neurology.

[3]  A. Efrat,et al.  NeuronMetrics: Software for semi-automated processing of cultured neuron images , 2007, Brain Research.

[4]  F. G. Worden,et al.  The neurosciences : fourth study program , 1979 .

[5]  Douglas B. Ehlenberger,et al.  Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser scanning microscopy images , 2006, Nature Protocols.

[6]  Floris G Wouterlood,et al.  3‐D Reconstruction of Neurons from Multichannel Confocal Laser Scanning Image Series , 2005, Current protocols in neuroscience.

[7]  R E Burke,et al.  A parsimonious description of motoneuron dendritic morphology using computer simulation , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  Damian J. Wallace,et al.  Automated axon length quantification for populations of labelled neurons , 2008, Journal of Neuroscience Methods.

[9]  R. C Cannon,et al.  An on-line archive of reconstructed hippocampal neurons , 1998, Journal of Neuroscience Methods.

[10]  Yong Zhang,et al.  A novel tracing algorithm for high throughput imaging Screening of neuron-based assays , 2007, Journal of Neuroscience Methods.

[11]  Xiaobo Zhou,et al.  Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays , 2007, NeuroImage.

[12]  M. Pool,et al.  NeuriteTracer: A novel ImageJ plugin for automated quantification of neurite outgrowth , 2008, Journal of Neuroscience Methods.

[13]  Khalid A. Al-Kofahi,et al.  Rapid automated three-dimensional tracing of neurons from confocal image stacks , 2002, IEEE Transactions on Information Technology in Biomedicine.

[14]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[15]  P. Saggau,et al.  Combining Optical Imaging and Computational Modeling to Analyze Structure and Function of Living Neurons , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Randy M Bruno,et al.  Transmitted light brightfield mosaic microscopy for three-dimensional tracing of single neuron morphology. , 2007, Journal of biomedical optics.

[17]  G. Ascoli,et al.  L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies , 2008, Nature Protocols.

[18]  D. Amaral,et al.  Dendritic morphology, local circuitry, and intrinsic electrophysiology of principal neurons in the entorhinal cortex of macaque monkeys , 2004, The Journal of comparative neurology.

[19]  Pengyu Hong,et al.  Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening , 2010, Neuroinformatics.

[20]  J F Evers,et al.  Progress in functional neuroanatomy: precise automatic geometric reconstruction of neuronal morphology from confocal image stacks. , 2005, Journal of neurophysiology.

[21]  E Meijering,et al.  Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[22]  D. Chklovskii,et al.  Class-Specific Features of Neuronal Wiring , 2004, Neuron.

[23]  Gabriel Wittum,et al.  The Tree-Edit-Distance, a Measure for Quantifying Neuronal Morphology , 2009, Neuroinformatics.

[24]  Ioannis A Kakadiaris,et al.  Live neuron morphology automatically reconstructed from multiphoton and confocal imaging data. , 2008, Journal of neurophysiology.

[25]  Giorgio A. Ascoli,et al.  Practical Aspects in Anatomically Accurate Simulations of Neuronal Electrophysiology , 2002 .

[26]  J. Selinummi,et al.  Three-Dimensional Digital Image Analysis of Immunostained Neurons in Thick Tissue Sections , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  G. Ascoli,et al.  NeuroMorpho.Org: A Central Resource for Neuronal Morphologies , 2007, The Journal of Neuroscience.

[28]  I. Konstantinidis,et al.  Frames-Based Denoising in 3D Confocal Microscopy Imaging , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[29]  Alexander Borst,et al.  The Morphological Identity of Insect Dendrites , 2008, PLoS Comput. Biol..

[30]  Armen Stepanyants,et al.  Detection of the optimal neuron traces in confocal microscopy images , 2009, Journal of Neuroscience Methods.

[31]  Badrinath Roysam,et al.  Improved detection of branching points in algorithms for automated neuron tracing from 3D confocal images , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[32]  Giorgio A Ascoli,et al.  Quantifying neuronal size: summing up trees and splitting the branch difference. , 2008, Seminars in cell & developmental biology.

[33]  Giorgio A. Ascoli,et al.  Neuroinformatics Grand Challenges , 2008, Neuroinformatics.

[34]  Bartlett W. Mel,et al.  Dendrites: bug or feature? , 2003, Current Opinion in Neurobiology.

[35]  Nobuyuki Matsui,et al.  Reconstruction and simulation for three-dimensional morphological structure of insect neurons , 2006, Neurocomputing.

[36]  W. Brent Lindquist,et al.  Automated Algorithms for Multiscale Morphometry of Neuronal Dendrites , 2004, Neural Computation.

[37]  Yong Zhang,et al.  3D Axon Structure Extraction and Analysis in Confocal Fluorescence Microscopy Images , 2008, Neural Computation.

[38]  Michael L. Hines,et al.  Semi-automated population of an online database of neuronal models (ModelDB) with citation information, using PubMed for validation , 2007, Neuroinformatics.

[39]  G. Ascoli.  Computational Neuroanatomy , 2002, Humana Press.

[40]  Changming Sun,et al.  Automated analysis of neurite branching in cultured cortical neurons using HCA‐Vision , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[41]  R. W. Draft,et al.  Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system , 2007, Nature.

[42]  Giorgio A. Ascoli,et al.  A Comparative Computer Simulation of Dendritic Morphology , 2008, PLoS Comput. Biol..

[43]  Jianli Li,et al.  Membrane Targeted Horseradish Peroxidase as a Marker for Correlative Fluorescence and Electron Microscopy Studies , 2009, Front. Neural Circuits.

[44]  Eric L. Miller,et al.  Automated Axon Tracking of 3D Confocal Laser Scanning Microscopy Images Using Guided Probabilistic Region Merging , 2007, Neuroinformatics.

[45]  Xiaobo Zhou,et al.  Automated neurite labeling and analysis in fluorescence microscopy images , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[46]  Khalid A. Al-Kofahi,et al.  Median-based robust algorithms for tracing neurons from noisy confocal microscope images , 2003, IEEE Transactions on Information Technology in Biomedicine.

[47]  J. van Pelt,et al.  Analysis of tubular structures in three-dimensional confocal images , 2002, Network.

[48]  Douglas B. Ehlenberger,et al.  New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales , 2005, Neuroscience.

[49]  D. R. Myatt,et al.  Three dimensional reconstruction of neurons , 2007 .

[50]  G A Ascoli,et al.  Generation, description and storage of dendritic morphology data. , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[51]  Chia-Ling Tsai,et al.  Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images , 2004, IEEE Transactions on Information Technology in Biomedicine.

[52]  Christof Koch,et al.  The role of single neurons in information processing , 2000, Nature Neuroscience.

[53]  Giorgio A Ascoli,et al.  Statistical determinants of dendritic morphology in hippocampal pyramidal neurons: A hidden Markov model , 2005, Hippocampus.

[54]  Alfredo Rodriguez,et al.  Three-dimensional neuron tracing by voxel scooping , 2009, Journal of Neuroscience Methods.

[55]  Ju Lu,et al.  Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images , 2009, PloS one.

[56]  Michael Scholz,et al.  New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks , 2004, NeuroImage.