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Open Access Highly Accessed Research article

A distance-field based automatic neuron tracing method

Jinzhu Yang12, Paloma T Gonzalez-Bellido23 and Hanchuan Peng24*

Author affiliations

1 Current address: Key Laboratory of Medical Image Computing, Ministry of Education, Northeastern University, Shenyang, China

2 Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

3 Current address: Marine Biological Laboratory, Woods Hole, MA, USA

4 Current address: Allen Institute for Brain Science, Seattle, WA, USA

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Citation and License

BMC Bioinformatics 2013, 14:93  doi:10.1186/1471-2105-14-93

Published: 12 March 2013



Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.


We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based “force” fields are used: one for “pressure”, which is the distance transform field of foreground pixels (voxels) to the background, and another for “thrust”, which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can “push” a “rolling ball” quickly along the skeleton of a neuron, reconstructing the 3D cell morphology.


We have used DF-Tracing to reconstruct the intricate neuron structures found in noisy image stacks, obtained with 3D laser microscopy, of dragonfly thoracic ganglia. Compared to several previous methods, DF-Tracing produces better reconstructions.