D.R. Myatt

NAPSAC: High Noise, High Dimensional Robust Estimation - it's in the Bag.

An umber of the most powerful robust estimation algorithms, such as RANSAC, MINPRAN and LMS ,h ave their basis in selecting random minimal sets of data to instantiate hypotheses. However, their performance degrades in higher dimensional spaces due to the exponentially decreasing probability of sampling a set that is composed entirely of inliers. In order to overcome this, rather than picking sets at random, a new strategy is proposed that alters the way samples are taken, under the assumption that inliers will tend to be closer to one another than outliers. Based on this premise, the NAPSAC (N Adjacent Points SAmple Consensus) algorithm is derived and its performance is shown to be superior to RANSAC in both high noise and high dimensional spaces.

Neuromantic – from Semi-Manual to Semi-Automatic Reconstruction of Neuron Morphology

The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Improved automatic midline tracing of neurites with Neuromantic

Background The accurate reconstruction of neuronal morphology from image stacks obtained via microscopy is of significant importance to computational neuroscience. Firstly, it facilitates the validation of models of neuronal behaviour by allowing for comparisons between electrophysiological testing and simulation (through NEURON [1], for example). Secondly, through the use of appropriate morphometrics [2], significant differences between the shape of neurons in control and experimental conditions may be identified, thus lending insight into low-level components of neurological diseases. Also, there is a high level of interest in the reconstruction of larger scale networks within the nervous system and the identification of patterns of connectivity.

Towards the Automatic Reconstruction of Dendritic Trees using Particle Filters

The 3D reconstruction of a Golgi-stained dendritic tree from a serial stack of images captured with a transmitted light bright-field microscope is investigated. Modifications to the boot-strap filter are discussed such that the tree structure may be estimated recursively as a series of connected segments. The tracking performance of the bootstrap particle filter is compared against Differential Evolution, an evolutionary global optimisation method, both in terms of robustness and accuracy. It is found that the particle filtering approach is significantly more robust and accurate for the data considered.