Hang Xiao1, Hanchuan Peng. 1. Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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.
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.
Authors: Paloma T Gonzalez-Bellido; Hanchuan Peng; Jinzhu Yang; Apostolos P Georgopoulos; Robert M Olberg Journal: Proc Natl Acad Sci U S A Date: 2012-12-03 Impact factor: 11.205
Authors: Kerry M Brown; Germán Barrionuevo; Alison J Canty; Vincenzo De Paola; Judith A Hirsch; Gregory S X E Jefferis; Ju Lu; Marjolein Snippe; Izumi Sugihara; Giorgio A Ascoli Journal: Neuroinformatics Date: 2011-09
Authors: Jasmine N Singh; Taylor M Nowlin; Gregory J Seedorf; Steven H Abman; Douglas P Shepherd Journal: J Biomed Opt Date: 2017-07-01 Impact factor: 3.170