Paper Info
Title
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 similar to 700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods.
Year
DOI
Venue
2013
10.1093/bioinformatics/btt170
BIOINFORMATICS
Field
DocType
Volume
Voxel,Codebase,Computational neuroscience,Source code,Computer science,Robustness (computer science),Software,Distance transform,Bioinformatics,Tracing
Journal
29
Issue
ISSN
Citations 
11
1367-4803
36
PageRank 
References 
Authors
1.03
10
2
Name
Order
Citations
PageRank
Hang Xiao1856.37
Hanchuan Peng23930182.27