Automated Tracing of Neurites from Light Microscopy Stacks of Images
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ORIGINAL ARTICLE
Automated Tracing of Neurites from Light Microscopy Stacks of Images Paarth Chothani & Vivek Mehta & Armen Stepanyants
Published online: 12 May 2011 # Springer Science+Business Media, LLC 2011
Abstract Automating the process of neural circuit reconstruction on a large-scale is one of the foremost challenges in the field of neuroscience. In this study we examine the methodology for circuit reconstruction from threedimensional light microscopy (LM) stacks of images. We show how the minimal error-rate of an ideal reconstruction procedure depends on the density of labeled neurites, giving rise to the fundamental limitation of an LM based approach for neural circuit research. Circuit reconstruction procedures typically involve steps related to neuron labeling and imaging, and subsequent image preprocessing and tracing of neurites. In this study, we focus on the last step—detection of traces of neurites from already pre-processed stacks of images. Our automated tracing algorithm, implemented as part of the Neural Circuit Tracer software package, consists of the following main steps. First, image stack is filtered to enhance labeled neurites. Second, centerline of the neurites is detected and optimized. Finally, individual branches of the optimal trace are merged into trees based on a cost minimization approach. The cost function accounts for branch orientations, distances between their end-points, curvature of the merged structure, and its intensity. The algorithm is capable of connecting branches which appear broken due to imperfect labeling and can resolve situations where branches appear to be fused due the limited resolution of light microscopy. The Neural Circuit Tracer software is designed to automatically incorporate ImageJ plug-ins and Paarth Chothani and Vivek Mehta contributed equally to this work P. Chothani : V. Mehta : A. Stepanyants (*) Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University, Boston, MA 02115, USA e-mail: [email protected]
functions written in MatLab and provides roughly a 10-fold increases in speed in comparison to manual tracing. Keywords Tracing . Segmentation . Axon . Dendrite . Confocal . Stack
Introduction It is evident, that the complete understanding of the brain function can only be derived from a substantially detailed account of synaptic connectivity in the underlying neural circuit. Can such an account be given in the form of a complete connectome of the circuit (Lichtman and Sanes 2008; Sporns et al. 2005)? While the connectomes of small invertebrate circuits can be fully determined electronmicroscopically (EM) (Chen et al. 2006; White et al. 1986), this technique, in spite of a number of impressive developments in recent years [see e.g. (Briggman and Denk 2006; Denk and Horstmann 2004; Hayworth et al. 2006; Mishchenko et al. 2010)], still lacks the capacity to reconstruct connectivity on a larger scale. Consider a hypothetical serial-section EM reconstruction of a 1 mm3 brain tissue. Volume of this siz
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