{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T09:46:19Z","timestamp":1784454379320,"version":"3.55.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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\u2019s 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 \u223c700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Contact: \u00a0hanchuanp@alleninstitute.org<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt170","type":"journal-article","created":{"date-parts":[[2013,4,20]],"date-time":"2013-04-20T02:09:34Z","timestamp":1366423774000},"page":"1448-1454","source":"Crossref","is-referenced-by-count":227,"title":["APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree"],"prefix":"10.1093","volume":"29","author":[{"given":"Hang","family":"Xiao","sequence":"first","affiliation":[{"name":"1 Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA, 2CAS-MPG Partner Institute for Computational Biology, Shanghai 200031, China and 3Allen Institute for Brain Science, Seattle, WA 98103, USA"},{"name":"1 Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA, 2CAS-MPG Partner Institute for Computational Biology, Shanghai 200031, China and 3Allen Institute for Brain Science, Seattle, WA 98103, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanchuan","family":"Peng","sequence":"additional","affiliation":[{"name":"1 Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA, 2CAS-MPG Partner Institute for Computational Biology, Shanghai 200031, China and 3Allen Institute for Brain Science, Seattle, WA 98103, USA"},{"name":"1 Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA, 2CAS-MPG Partner Institute for Computational Biology, Shanghai 200031, China and 3Allen Institute for Brain Science, Seattle, WA 98103, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,4,19]]},"reference":[{"key":"2023062610153233600_btt170-B1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TITB.2002.1006304","article-title":"Rapid automated three-dimensional tracing of neurons from confocal image stacks","volume":"6","author":"Al-Kofahi","year":"2002","journal-title":"IEEE Trans. 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