The All-Path-Pruning-2 paper is published in BioinformaticsPosted By: Hanchuan Peng - Apr 20, 2013
Tool/Resource: Vaa3D and Vaa3D-Neuron
The paper for the just released source code of the automated 3D neuron tracing method is published.
APP2: Automatic Tracing of 3D Neuron Morphology Based on Hierarchical Pruning of Gray-Weighted Image Distance-Trees
Hang Xiao1,2,4 and
+ Author Affiliations
1 Janelia Farm Research Campus, Howard Hughes Medical Institute
2 CAS-MPG Partner Institute for Computational Biology
3 Allen Institute for Brain Science
4 Equal contribution
↵*To whom correspondence should be addressed. Hanchuan Peng, E-mail: firstname.lastname@example.org
Motivation: Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images.
Results: We developed APP2 (all-path-pruning 2.0) for 3D neuron tracing. The most important idea is to prune an initial reconstruction tree of a neuron's morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. To further enhance the robustness of APP2, we compute the distance-transform of all image-voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance-transform. We also design a fast-marching algorithm based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows us to trace large images. We bench-tested APP2 on about 700 3D microscopic images and found APP2 can generate more satisfactory results in most cases than several previous methods.
Availability: The software has been implemented as an open-source Vaa3D plugin. The source code is available in the Vaa3D code repository http://vaa3d.org.
* Contact: email@example.com