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Automated neuron tracing using probability hypothesis density filtering.

Miroslav Radojevic, Erik Meijering

Bioinformatics (Oxford, England)

|January 10, 2017

Summary

This summary is machine-generated.

A new method traces neuron centerlines for digital reconstruction, improving accuracy in neurobiological studies. This approach, based on Bayesian tracking, offers comparable or superior performance to existing techniques.

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Published on: February 2, 2021

Area of Science:

Neuroscience
Computational Biology
Image Analysis

Background:

Neuronal function is linked to cell morphology, making accurate digital reconstruction crucial for neurobiological research.
Existing methods for reconstructing neuronal morphology from microscopy data have limitations, necessitating improved approaches.

Purpose of the Study:

To introduce a novel method for tracing neuron centerlines essential for complete neuronal reconstruction.
To address the need for more accurate and efficient digital reconstruction of neuronal morphology.

Main Methods:

Neuron tracing is framed as a Bayesian multi-object tracking problem.
Probability hypothesis density filtering is employed to solve the tracking problem.
The method was implemented as an ImageJ plugin using Java.

Main Results:

The proposed method demonstrates performance comparable to or exceeding state-of-the-art techniques.
Experiments were conducted on both 2D and 3D fluorescence microscopy image datasets of real neurons.
The approach offers a fundamentally different strategy compared to previous neuron tracing methods.

Conclusions:

The developed method provides a robust solution for neuron centerline tracing.
This advancement contributes to more accurate digital reconstruction of neuronal morphology.
The software is freely available for non-commercial use, facilitating further research.