APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree.

ABSTRACT:

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 all-path-pruning 2.0 (APP2) 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 ~700 3D microscopic images and found that 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.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Xiao H 

PROVIDER: S-EPMC3661058 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature