Neuron tracing in perspective

Abstract

The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. ' 2010 International Society for Advancement of Cytometry

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

“…To eliminate this bias and boost throughput, automation of image analysis is inevitable. However, the design and implementation of generic automated image analyses are non-trivial since the experimental conditions, such as microscope settings, type of stains, cell type and cell densities that are used, introduce a strong variability in image quality 35 . Nevertheless, with sufficient standardization of the sample preparation and image acquisition protocols, and adequate pre-processing of the raw image datasets, the major correlates of neuronal connectivity can be quantified in an unbiased way.…”

“…Quantification of the internal structure or folding of the nucleus may thus serve as a readout for neuronal connectivity. Nucleus segmentation is often included in neuronal image analysis pipelines as a starting point for segmenting cell bodies and/or neurites 35 . From segmented nuclei in 2D images, nuclear shape descriptors, such as surface and circularity, can easily be derived using general object enhancement and thresholding procedures.…”

Extracting information from imaging cytometry: a review

“…Neuron morphology presents a challenging problem for quantitation, because the structures are complex and often overlap in 3D, which confounds the analysis. Several methods now exist to address these problems and can be used to measure features of neurons (Meijering 2010). …”

Three-dimensional neurite tracing under globally varying contrast

“…The first steps towards encoding such information into a compact numerical representation is locating the neuron in images, identifying its parts (soma, axon, dendrites), and tracing axon and dendrite branches (what we summarize with the term neurite) [8]. The latter task has been a major challenge mainly due to imaging device limitations, image noise, as well as structural complexity and variability of the neurites.…”

“…There is a large number of neurite reconstruction approaches, many of which have been reviewed in [8] and [11]. They can be categorized into global, local, and combinations of the two.…”