Automated Neuron Tracing Using Probability Hypothesis Density Filtering

M. Radojević and E. Meijering

Bioinformatics, vol. 33, no. 7, April 2017, pp. 1073-1080

DOI PubMed

Motivation: The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed.

Results: Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi-object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image data sets of real neurons indicate the proposed method performs comparably or even better than the state of the art.

Availability and Implementation: Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non-commercial use from Bitbucket.

Copyright © 2017 by the authors. Published version © 2017 by Oxford Journals. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

Copyright © 1996 - 2017 Erik Meijering