ArticleBioinformatics (Oxford, England)2013
APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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105 citing papers in PubMed, 240 citations in OpenAlex.
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- A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.Patterns (New York, N.Y.) · 2024Article
45 more citing papers are in PubMed but not listed here.
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2 authors at 1 institution in 1 country.
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Abstract
motivationTracing 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.
resultsWe 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.
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