General Information
| Description | Automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree |
| Homepage | http://vaa3d.org |
Publications
APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree
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.
Contact:
hanchuanp@alleninstitute.org
Supplementary information:
Supplementary data are available at Bioinformatics online.
Related Tools
HTML5 PivotViewer
Published May 2014
High-throughput visualization and querying of image data on the web
fastER
Published February 2017
A user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.
