RESUMO
The communication between two neurons is established by endogenous chemical particles aggregated in vesicles that move along the axons. It is known that an abnormal transport of these vesicles is correlated with neurodegenerative diseases. The quantification of the dynamics of vesicles movement can therefore be a window to study early detection of such diseases. Nevertheless, most of the studies in the literature rely on manual tracking techniques. In this paper we present a novel methodology for quantifying neurotransmitter vesicle dynamics by using a combination of image tracking and classification algorithms. We use confocal microscopy videos of living neurons to detect and classify vesicles using support vector machine (SVM), while motion is extracted via global nearest neighbor (GNN) tracking approach. Results of the classification algorithm are presented and compared to a ground truth dataset defined by experts. Sensitivity of 90% and specificity of 97% were obtained at a much lower computational cost than an established method from the literature (0.24s/frame vs. 125s/frame). These preliminary results suggest the great potential of the method and tool we have been developing for single particle movement dynamics measure in living cells.
