@article {46813,
	title = {BlastNeuron for automated comparison, retrieval and clustering of 3D neuron morphologies.},
	journal = {Neuroinformatics},
	volume = {13},
	year = {2015},
	month = {2015 Jun 3},
	pages = {487-99},
	abstract = {<p>Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.</p>
},
	issn = {1559-0089},
	doi = {10.1007/s12021-015-9272-7},
	author = {Wan, Yinan and Long, Fuhui and Qu, Lei and Xiao, Hang and Hawrylycz, Michael and Myers, Eugene W and Peng, Hanchuan}
}
@article {45064,
	title = {Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis.},
	journal = {Nature Communications},
	volume = {5},
	year = {2014},
	month = {2014 Jul 11},
	pages = {4342},
	abstract = {<p>Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.</p>
},
	issn = {2041-1723},
	doi = {10.1038/ncomms5342},
	author = {Peng, Hanchuan and Tang, Jianyong and Xiao, Hang and Bria, Alessandro and Zhou, Jianlong and Butler, Victoria and Zhou, Zhi and Gonzalez-Bellido, Paloma T and Oh, Seung W and Chen, Jichao and Mitra, Ananya and Tsien, Richard W and Zeng, Hongkui and Ascoli, Giorgio A and Iannello, Giulio and Hawrylycz, Michael and Myers, Eugene and Long, Fuhui}
}
@article {6101,
	title = {APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree.},
	journal = {Bioinformatics (Oxford, England)},
	volume = {29},
	year = {2013},
	month = {2013 Jun 1},
	pages = {1448-54},
	abstract = {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.},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/btt170},
	author = {Xiao, Hang and Peng, Hanchuan}
}
