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thesis.bib
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% vim: fdm=marker
%% Methods {{{
@inproceedings{Bas2011,
abstract = {Blood vessel segmentation, that is, extraction of the
center lines and corresponding local cylinder radii are
important for the study of vascular diseases, and in
the brain also important for the modeling and
understanding of relationships between hemodynamics and
electrical neural activity. Several image processing
methods have been proposed for vessel extraction in
many domains including those that explore the use of
pattern recognition techniques, model-based approaches,
tracking based approaches, artificial based approaches, neural
network based approaches, and miscellaneous tube-like
object detection approaches. In this paper, we propose
a ridge tracing approach based on recently developed
principal curve (PC) projection and tracing algorithms
for the extraction of vasculature networks in the brain
from 3D microscopy image stacks. Results on
mice brain imagery obtained for the purpose of studying
hemodynamic effects on neural activity are promising.},
author = {Bas, Erhan and Ghadarghadar, Nastaran and Erdogmus, Deniz},
booktitle = {Proc. {IEEE} International Symposium on Biomedical Imaging},
x_publisher = {IEEE},
doi = {10.1109/ISBI.2011.5872652},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bas, Ghadarghadar, Erdogmus - 2011 - Automated extraction of blood vessel networks from 3D microscopy image stacks via multi-scale principal curve tracing.pdf:pdf},
isbn = {9781424441280},
issn = {19457928},
keywords = {3D tube extraction,Vessel tracing,bifurcation detection,principal curves},
mendeley-groups = {Zotero - Zotero Library,Zotero - computer vision,Zotero - !!!00000\_connecting\_centerlines},
pages = {1358--1361},
title = {Automated extraction of blood vessel networks from 3{D} microscopy image stacks via multi-scale principal curve tracing},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5872652},
year = {2011}
}
@article{Bauer2010,
author = {Bauer, Christian and Pock, Thomas and Sorantin, Erich and Bischof, Horst and Beichel, Reinhard},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bauer et al. - 2010 - Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts.pdf:pdf},
journal = {Medical Image Analysis},
mendeley-groups = {Zotero - Zotero Library,Zotero - computer vision,Zotero - !!!00000\_connecting\_centerlines},
number = {2},
pages = {172--184},
title = {Segmentation of interwoven 3{D} tubular tree structures utilizing shape priors and graph cuts},
url = {http://www.sciencedirect.com/science/article/pii/S1361841509001406},
volume = {14},
year = {2010}
}
@article{MIA-anisotropic-path-searching-Xie2011,
author = {Xie, Jun and Zhao, Ting and Lee, Tzumin and Myers, Eugene and Peng, Hanchuan},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Xie et al. - 2011 - Anisotropic path searching for automatic neuron reconstruction.pdf:pdf},
journal = {Medical Image Analysis},
number = {5},
pages = {680--689},
title = {Anisotropic path searching for automatic neuron reconstruction},
url = {http://www.sciencedirect.com/science/article/pii/S1361841511000776},
volume = {15},
year = {2011}
}
@inproceedings{MICCAI-anisotropic-path-searching-Xie2010,
author = {Xie, Jun and Zhao, Ting and Lee, Tzumin and Myers, Eugene and Peng, Hanchuan},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Xie et al. - 2010 - Automatic neuron tracing in volumetric microscopy images with anisotropic path searching.pdf:pdf},
pages = {472--479},
publisher = {Springer},
title = {Automatic neuron tracing in volumetric microscopy images with anisotropic path searching},
url = {http://link.springer.com/chapter/10.1007/978-3-642-15745-5_58},
year = {2010},
booktitle = {Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention}
}
@article{Open-Curve-Snake-Wang2011,
abstract = {This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.},
author = {Wang, Yu and Narayanaswamy, Arunachalam and Tsai, Chia-Ling and Roysam, Badrinath},
doi = {10.1007/s12021-011-9110-5},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang et al. - 2011 - A broadly applicable 3-D neuron tracing method based on open-curve snake(3).pdf:pdf;:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang et al. - 2011 - A broadly applicable 3-D neuron tracing method based on open-curve snake(4).pdf:pdf},
issn = {1559-0089},
journal = {Neuroinformatics},
keywords = {Algorithms,Animals,Computer-Assisted,Computer-Assisted: methods,Computer-Assisted: trends,Image Processing,Imaging,Mice,Neuroanatomical Tract-Tracing Techniques,Neuroanatomical Tract-Tracing Techniques: methods,Neuroanatomical Tract-Tracing Techniques: trends,Neurons,Neurons: cytology,Neurons: physiology,Rats,Software,Software: standards,Software: trends,Three-Dimensional,Three-Dimensional: methods,Three-Dimensional: trends},
month = {sep},
number = {2-3},
pages = {193--217},
pmid = {21399937},
title = {A broadly applicable 3-{D} neuron tracing method based on open-curve snake},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21399937},
volume = {9},
year = {2011}
}
@inproceedings{ORION_Santamaria-Pang2007,
abstract = {In this paper, we present a general framework for extracting 3D centerlines from volumetric datasets. Unlike the majority of previous approaches, we do not require a prior segmentation of the volume nor we do assume any particular tubular shape. Centerline extraction is performed using a morphology-guided level set model. Our approach consists of: i) learning the structural patterns of a tubular-like object, and ii) estimating the centerline of a tubular object as the path with minimal cost with respect to outward flux in gray level images. Such shortest path is found by solving the Eikonal equation. We compare the performance of our method with existing approaches in synthetic, CT, and multiphoton 3D images, obtaining substantial improvements, especially in the case of irregular tubular objects.},
author = {Santamar{\'i}a-Pang, a and Colbert, C M and Saggau, P and Kakadiaris, I a},
booktitle = {Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention},
doi = {10.1007/978-3-540-75759-7_59},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Santamar\'ia-Pang et al. - 2007 - Automatic centerline extraction of irregular tubular structures using probability volumes from multiphoton imaging.pdf:pdf},
isbn = {978-3-540-75758-0},
issn = {03029743},
pages = {486--494},
pmid = {18044604},
publisher = {Springer},
title = {Automatic centerline extraction of irregular tubular structures using probability volumes from multiphoton imaging},
url = {http://link.springer.com/chapter/10.1007/978-3-540-75759-7_59},
volume = {10},
year = {2007}
}
@article{ORION_Santamaria-Pang2015,
abstract = {The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from hundreds of microns to hundreds of nanometers. In this paper, we address these challenges and present a computational framework for an automatic, three-dimensional (3D) morphological reconstruction of live nerve cells. The key aspects of this approach are: (i) detection of neuronal dendrites through learning 3D tubular models, and (ii) skeletonization by a new algorithm using a morphology-guided deformable model for extracting the dendritic centerline. To represent the neuron morphology, we introduce a novel representation, the Minimum Shape-Cost (MSC) Tree that approximates the dendrite centerline with sub-voxel accuracy and demonstrate the uniqueness of such a shape representation as well as its computational efficiency. We present extensive quantitative and qualitative results that demonstrate the accuracy and robustness of our method.},
author = {Santamar{\'i}a-Pang, Alberto and Hernandez-Herrera, Paul and Papadakis, Manos and Saggau, Peter and Kakadiaris, Ioannis A.},
doi = {10.1007/s12021-014-9253-2},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Santamar\'ia-Pang et al. - 2015 - Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using.pdf:pdf},
volume = {13},
number = {3},
issn = {1539-2791, 1559-0089},
journal = {Neuroinformatics},
x_language = {en},
month = {jan},
pmid = {25631538},
title = {Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using {3D} Tubular Models},
url = {http://link.springer.com/10.1007/s12021-014-9253-2},
year = {2015}
}
@techreport{Jeong2015,
title = {Inference of Curvilinear Structure based on Learning a Ranking Function and Graph Theory},
file = {:tmp/RR-8789.pdf:pdf},
author = {Jeong, Seong-Gyun and Tarabalka, Yuliya and Nisse, Nicolas and Zerubia, Josiane},
url = {https://hal.inria.fr/hal-01214932},
type = {Research Report},
number = {RR-8789},
institution = {{Inria Sophia Antipolis}},
year = {2015},
pdf = {https://hal.inria.fr/hal-01214932/file/RR-8789.pdf},
hal_id = {hal-01214932},
hal_version = {v1},
}
@article{Luo2015,
author = {Luo, Gongning and Sui, Dong and Wang, Kuanquan and Chae, Jinseok},
doi = {10.1186/s12859-015-0780-0},
file = {:tmp/s12859-015-0780-0.pdf:pdf},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Neuron anatomy structure reconstruction,Radius est,neuron anatomy structure reconstruction,open curve snake model,radius estimation,sliding filter},
number = {1},
pages = {342},
publisher = {BMC Bioinformatics},
title = {Neuron anatomy structure reconstruction based on a sliding filter},
url = {http://www.biomedcentral.com/1471-2105/16/342},
volume = {16},
year = {2015}
}
@article{De2015,
pubstate = {forthcoming},
author = {De, Jaydeep and Cheng, Li and Zhang, Xiaowei and Lin, Feng and Li, Huiqi and Ong, Kok and Yu, Weimiao and Yu, Yuanhong and Ahmed, Sohail},
doi = {10.1109/TMI.2015.2465962},
file = {:tmp/07219463.pdf:pdf},
issn = {0278-0062},
journal = {{IEEE} {T}ransactions on {M}edical {I}maging},
x_publisher = {IEEE},
title = {A Graph-Theoretical Approach for Tracing Filamentary Structures in Neuronal and Retinal Images},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7219463},
volume = {0062},
year = {2015}
}
@inproceedings{Gulyanon2015,
author = {Gulyanon, S and Sharifai, N and Bleykhman, S and Kelly, E and Kim, M D and Chiba, A and Tsechpenakis, G},
file = {:tmp/07164010.pdf:pdf},
isbn = {9781479923748},
pages = {875--879},
title = {Three-Dimensional Neurite Tracing Under Globally Varying Contrast},
booktitle = {Proc. {IEEE} $12^{th}$ International Symposium on Biomedical Imaging},
x_organization={IEEE},
year = {2015}
}
@article{Mukherjee2014,
abstract = {A segmentation framework is proposed to trace neurons from confocal microscopy images. With an increasing demand for high throughput neuronal image analysis, we propose an automated scheme to perform segmentation in a variational framework. Our segmentation technique, called tubularity flow field (TuFF) performs directional regional growing guided by the direction of tubularity of the neurites. We further address the problem of sporadic signal variation in confocal microscopy by designing a local attraction force field which is able to bridge the gaps between local neurite fragments, even in the case of complete signal loss. Segmentation is performed in an integrated fashion by incorporating the directional region growing and the attraction force based motion in a single framework using level sets. This segmentation is accomplished without manual seed point selection; it is automated. The performance of TuFF is demonstrated over a set of 2-D and 3-D confocal microscopy images where we report an improvement of above 75{\%} in terms of mean absolute error over three extensively used neuron segmentation algorithms. Two novel features of the variational solution, the evolution force and the attraction force, hold promise as contributions that can be employed in a number of image analysis applications.},
author = {Mukherjee, Suvadip and Condron, Barry and Acton, Scott},
doi = {10.1109/TIP.2014.2378052},
file = {:tmp/06975188.pdf:pdf},
isbn = {1057-7149 VO - 24},
issn = {1941-0042},
journal = {{IEEE} {T}ransactions on {I}mage {P}rocessing},
x_publisher = {IEEE},
number = {1},
pages = {374--389},
pmid = {25494506},
title = {Tubularity Flow Field - A Technique For Automatic Neuron Segmentation},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25494506},
volume = {24},
year = {2014}
}
@inproceedings{Hernandez-Herrera2014,
author = {Hernandez-Herrera, Paul and Papadakis, Manos and Kakadiaris, Ioannis A},
file = {:tmp/PHH{\_}OCCSEN{\_}ISBI2014.pdf:pdf},
isbn = {9781467319614},
keywords = {Biomedical Image Processing,Classification methods,Image segmentation other},
pages = {1316--1319},
title = {Segmentation of Neurons based on One-Class Classification},
booktitle = {Proc. {IEEE} $11^{th}$ International Symposium on Biomedical Imaging},
x_organization = {IEEE},
year = {2014}
}
@inproceedings{Basu2014,
author = {Basu, Sreetama and Racoceanu, Daniel},
doi = {10.1109/ICIP.2014.7025730},
file = {:tmp/07025730.pdf:pdf},
isbn = {978-1-4799-5751-4},
journal = {Proc. {IEEE} International Conference on Image Processing},
x_organization = {IEEE},
pages = {3597--3601},
title = {Reconstructing neuronal morphology from microscopy stacks using fast marching},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7025730},
year = {2014}
}
@article{Xiao2013,
author = {Xiao, H. and Peng, H.},
doi = {10.1093/bioinformatics/btt170},
file = {:tmp/Bioinformatics-2013-Xiao-1448-54.pdf:pdf},
issn = {1367-4803},
journal = {Bioinformatics},
number = {11},
pages = {1448--1454},
title = { {{APP2}} : automatic tracing of {3D} neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree},
url = {http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btt170},
volume = {29},
year = {2013}
}
@inproceedings{Jimenez2013,
abstract = {Centerline tracing of dendritic structures in confocal images of neurons is an essential tool for the construction of a geometric representation of a neuronal network. In this paper, we propose a novel algorithm (ORION 2) for centerline ex- traction that is both highly accurate and computationally effi- cient. The main novelty of the proposed method is the use of a small set of Multiscale Isotropic Laplacian filters for a fast and efficient binarization of the dendritic structure.The per- formance of this algorithm, which is validated using datasets from DIADEM is shown to be very competitive against other state-of-the-art algorithms.},
author = {Jim{\'{e}}nez, David and Papadakis, Manos and Labate, Demetrio and Kakadiaris, Ioannis A},
doi = {10.1109/ISBI.2013.6556658},
file = {:tmp/06556658.pdf:pdf},
isbn = {978-1-4673-6455-3},
issn = {1559-0089},
booktitle = {Proc. {IEEE} $10^{th}$ International Symposium on Biomedical Imaging},
x_organization = {IEEE},
pages = {1050--1053},
pmid = {25433514},
title = {Improved automatic centerline tracing for dendritic structures},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6556658},
year = {2013}
}
@inproceedings{Basu2013,
abstract = {Tubular structures are frequently encountered in bio-medical images. The center-lines of these tubules provide an accurate representation of the topology of the structures. We introduce a stochastic Marked Point Process framework for fully automatic extraction of tubular structures requiring no user interaction or seed points for initialization. Our Marked Point Process model enables unsupervised network extraction by fitting a configuration of objects with globally optimal associated energy to the centreline of the arbors. For this purpose we propose special configurations of marked objects and an energy function well adapted for detection of 3D tubular branches. The optimization of the energy function is achieved by a stochastic, discrete-time multiple birth and death dynamics. Our method finds the centreline, local width and orientation of neuronal arbors and identifies critical nodes like bifurcations and terminals. The proposed model is tested on 3D light microscopy images from the DIADEM data set with promising results.},
author = {Basu, Sreetama and Kulikova, Maria and Zhizhina, Elena and Ooi, Wei Tsang and Racoceanu, Daniel},
doi = {10.1007/978-3-642-40811-3_50},
file = {:tmp/chp{\%}3A10.1007{\%}2F978-3-642-40811-3{\_}50.pdf:pdf},
isbn = {9783642408106},
issn = {03029743},
booktitle = {Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention},
keywords = {*Algorithms,*Image Interpretation,*Microscopy/mt [Methods],*Neurons/cy [Cytology],*Pattern Recognition,Automated/mt [Methods],Cells,Computer Simulation,Computer-Assisted/mt [Metho,Cultured,Humans,Image Enhancement/mt [Methods],Models,Neurological,Reproducibility of Results,Sensitivity and Specificity,Statistical,Stochastic Processes},
pages = {396--403},
pmid = {24505691},
title = {A stochastic model for automatic extraction of {3D} neuronal morphology},
url = {http://link.springer.com/chapter/10.1007/978-3-642-40811-3_50},
volume = {16},
year = {2013}
}
@inproceedings{Mukherjee2013,
author = {Mukherjee, Suvadip and Acton, Scott T.},
booktitle = {Proc. {IEEE} International Conference on Image Processing},
x_publisher = {IEEE},
doi = {10.1109/ICIP.2013.6738137},
file = {:tmp/06738137.pdf:pdf},
isbn = {978-1-4799-2341-0},
month = {sep},
number = {1},
pages = {665--669},
title = {Vector field convolution medialness applied to neuron tracing},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6738137},
volume = {0},
year = {2013}
}
@incollection{Hernandez-Herrera2013,
author = {Hernandez-Herrera, Paul and Jim{\'{e}}nez, David and Kakadiaris, IoannisA. and Koutsogiannis, Andreas and Labate, Demetrio and Laezza, Fernanda and Papadakis, Manos},
file = {:tmp/a-harmonic-analysis-view-on-neuroscience-imaging.pdf:pdf},
isbn = {978-0-8176-8378-8},
keywords = {a,and i,and phrases,approximation er-,computational biomedicine lab,computer science,confocal microscopy,d,dendritic arbor segmentation,department of,directional aliasing,hernandez,herrera,houston,jim,kakadiaris are with the,p,ror,synthetic dendrites,synthetic tubular data,texas 77204,university of houston,usa},
pages = {423--450},
title = {A Harmonic Analysis View on Neuroscience Imaging},
url = {http://dx.doi.org/10.1007/978-0-8176-8379-5_21},
booktitle = {Excursions in Harmonic Analysis, Volume 2},
publisher = {Springer},
year = {2013}
}
@article{Ming2013,
abstract = {Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.},
author = {Ming, Xing and Li, Anan and Wu, Jingpeng and Yan, Cheng and Ding, Wenxiang and Gong, Hui and Zeng, Shaoqun and Liu, Qian},
doi = {10.1371/journal.pone.0084557},
editor = {Lytton, William W.},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ming et al. - 2013 - Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling(2).pdf:pdf},
issn = {19326203},
journal = {{PLoS ONE}},
x_language = {en},
month = {dec},
number = {12},
pages = {e84557},
pmid = {24391966},
title = {Rapid reconstruction of {3D} neuronal morphology from light microscopy images with augmented rayburst sampling},
url = {http://dx.plos.org/10.1371/journal.pone.0084557},
volume = {8},
year = {2013}
}
@article{Lee2012,
abstract = {Drosophila melanogaster is a well-studied model organism, especially in the field of neurophysiology and neural circuits. The brain of the Drosophila is small but complex, and the image of a single neuron in the brain can be acquired using confocal microscopy. Analyzing the Drosophila brain is an ideal start to understanding the neural structure. The most fundamental task in studying the neural network of Drosophila is to reconstruct neuronal structures from image stacks. Although the fruit fly brain is small, it contains approximately 100,000 neurons. It is impossible to trace all the neurons manually. This study presents a high-throughput algorithm for reconstructing the neuronal structures from 3D image stacks collected by a laser scanning confocal microscope. The proposed method reconstructs the neuronal structure by applying the shortest path graph algorithm. The vertices in the graph are certain points on the 2D skeletons of the neuron in the slices. These points are close to the 3D centerlines of the neuron branches. The accuracy of the algorithm was verified using the DIADEM data set. This method has been adopted as part of the protocol of the FlyCircuit Database, and was successfully applied to process more than 16,000 neurons. This study also shows that further analysis based on the reconstruction results can be performed to gather more information on the neural network.},
author = {Lee, Ping-Chang and Chuang, Chao-Chun and Chiang, Ann-Shyn and Ching, Yu-Tai},
doi = {10.1371/journal.pcbi.1002658},
file = {:tmp/journal.pcbi.1002658.pdf:pdf},
isbn = {1553-7358},
issn = {1553-7358},
journal = {PLoS Computational Biology},
keywords = {Animals,Brain,Brain: cytology,Computer Simulation,Drosophila melanogaster,Drosophila melanogaster: cytology,Image Interpretation, Computer-Assisted,Image Interpretation, Computer-Assisted: methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Models, Anatomic,Models, Neurological,Nerve Net,Nerve Net: cytology,Neurons,Neurons: cytology},
number = {9},
pages = {e1002658},
pmid = {23028271},
title = {High-throughput computer method for {3D} neuronal structure reconstruction from the image stack of the Drosophila brain and its applications.},
url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002658},
volume = {8},
year = {2012}
}
@article{Czarnecki2012,
abstract = {The developed model consists of a multilayer neural network with$\backslash$nreceptive fields used to estimate the local direction of the neuron on a$\backslash$nfragment of microscopy image. It can be used in a wide range of$\backslash$nclassical neuron reconstruction methods (manual, semi-automatic, local$\backslash$nautomatic or global automatic), some of which are also outlined in this$\backslash$npaper. The model is trained on an automatically generated training set$\backslash$nextracted from a provided example image stack and corresponding$\backslash$nreconstruction file. During the experiments the model was tested in$\backslash$nsimple statistical tests and in real applications, and achieved good$\backslash$nresults. The main advantage of the proposed approach is its simplicity$\backslash$nfor the end-user, one who might have little or no mathematical/computer$\backslash$nscience background, as it does not require any manual configuration of$\backslash$nconstants.},
author = {Czarnecki, Wojciech},
file = {:tmp/chp{\%}3A10.1007{\%}2F978-3-642-29350-4{\_}29.pdf:pdf},
isbn = {978-3-642-29349-8},
issn = {0302-9743},
journal = {{A}rtificial {I}ntelligence and {S}oft {C}omputing, {P}t {II}},
keywords = {artificial neural network,computer vision,neuron reconstruction,receptive fields},
pages = {242--250},
title = {Multilayer Neural Networks with Receptive Fields as a Model for the Neuron Reconstruction Problem},
volume = {7268},
year = {2012}
}
%%}}}
%% Metrics {{{
@article{DIADEM-metric-Gillette2011,
abstract = {Digital reconstructions of neuronal morphology are used to
study neuron function, development, and responses to various
conditions. Although many measures exist to analyze
differences between neurons, none is particularly
suitable to compare the same arborizing structure over
time (morphological change) or reconstructed by
different people and/or software (morphological error).
The metric introduced for the DIADEM (DIgital
reconstruction of Axonal and DEndritic Morphology)
Challenge quantifies the similarity between two
reconstructions of the same neuron by matching the
locations of bifurcations and terminations as well as
their topology between the two reconstructed arbors.
The DIADEM metric was specifically designed to capture
the most critical aspects in automating neuronal
reconstructions, and can function in feedback loops
during algorithm development. During the Challenge, the
metric scored the automated reconstructions of
best-performing algorithms against manually traced gold
standards over a representative data set collection.
The metric was compared with direct quality assessments
by neuronal reconstruction experts and with clocked
human tracing time saved by automation. The results
indicate that relevant morphological features were
properly quantified in spite of subjectivity in the
underlying image data and varying research goals. The
DIADEM metric is freely released open source
(http://diademchallenge.org) as a flexible instrument
to measure morphological error or change in
high-throughput reconstruction projects.},
author = {Gillette, Todd A. and Brown, Kerry M. and Ascoli, Giorgio A.},
doi = {10.1007/s12021-011-9117-y},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/VQ5D2NJZ/Gillette et al\_2011\_The DIADEM Metric.pdf:pdf},
issn = {1539-2791, 1559-0089},
journal = {Neuroinformatics},
keywords = {Algorithm,Automation,Axon,Computational neuroanatomy,Dendrite,Digital tracing,Morphology,Optical imaging},
x_language = {en},
month = {sep},
number = {2-3},
pages = {233--245},
pmid = {21519813},
shorttitle = {The DIADEM Metric},
title = {The {DIADEM} Metric: Comparing Multiple Reconstructions of the Same Neuron},
url = {http://link.springer.com/10.1007/s12021-011-9117-y},
volume = {9},
year = {2011}
}
@article{btmorph-Torben-Nielsen2014,
author = {Torben-Nielsen, Benjamin},
doi = {10.1007/s12021-014-9232-7},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/DUWZTBUP/Torben-Nielsen\_2014\_An Efficient and Extendable Python Library to Analyze Neuronal Morphologies.pdf:pdf},
issn = {1539-2791, 1559-0089},
journal = {Neuroinformatics},
x_language = {en},
month = {oct},
number = {4},
pages = {619--622},
pmid = {24924300},
title = {An Efficient and Extendable {P}ython Library to Analyze Neuronal Morphologies},
url = {http://link.springer.com/10.1007/s12021-014-9232-7},
volume = {12},
year = {2014}
}
@article{Lmeasure-Scorcioni2008,
author = {Scorcioni, Ruggero and Polavaram, Sridevi and Ascoli, Giorgio A},
doi = {10.1038/nprot.2008.51},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/5SKRD9IT/Scorcioni et al\_2008\_L-Measure.pdf:pdf},
issn = {1754-2189, 1750-2799},
journal = {Nature Protocols},
month = {apr},
number = {5},
pmid = {18451794},
pages = {866--876},
shorttitle = {L-Measure},
title = {{L}-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies},
url = {http://www.nature.com/doifinder/10.1038/nprot.2008.51},
volume = {3},
year = {2008}
}
@article{TREES_toolbox_Cuntz2010,
author = {Cuntz, Hermann and Forstner, Friedrich and Borst, Alexander and H\"{a}usser, Michael},
doi = {10.1371/journal.pcbi.1000877},
editor = {Morrison, Abigail},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/9VE9B8C5/Cuntz et al\_2010\_One Rule to Grow Them All.pdf:pdf},
issn = {1553-7358},
journal = {PLoS Computational Biology},
x_language = {en},
month = {aug},
number = {8},
pages = {e1000877},
shorttitle = {One Rule to Grow Them All},
title = {One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application},
url = {http://dx.plos.org/10.1371/journal.pcbi.1000877},
volume = {6},
year = {2010}
}
@inproceedings{Mayerich2011,
abstract = {One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitiatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.},
author = {Mayerich, David and Bjornsson, Chris and Taylor, Jonothan and Roysam, Badrinath},
booktitle = {Proc. {IEEE} Symposium on Biological Data Visualization},
x_publisher = {IEEE},
doi = {10.1109/BioVis.2011.6094051},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/FMCCNDRA/Mayerich et al\_2011\_Metrics for comparing explicit representations of interconnected biological.pdf:pdf},
isbn = {9781467300025},
keywords = {segmentation,skeletonization,validation,vector tracking},
pages = {79--86},
title = {Metrics for comparing explicit representations of interconnected biological networks},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6094051},
year = {2011}
}
@article{Mayerich2012,
abstract = {One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.},
author = {Mayerich, David and Bjornsson, Chris and Taylor, Jonathan and Roysam, Badrinath},
doi = {10.1186/1471-2105-13-S8-S7},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/I5UHIVTB/Mayerich et al\_2012\_NetMets.pdf:pdf},
isbn = {1471-2105},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Algorithms,Animals,Astrocytes,Astrocytes: cytology,Biological,Brain,Brain: blood supply,Brain: cytology,Cerebellum,Cerebellum: cytology,Computer-Assisted,Humans,Image Processing,Mice,Models,Nerve Net,Neurons,Neurons: cytology,Purkinje Cells,Purkinje Cells: cytology,Software},
number = {Suppl 8},
pages = {S7},
pmid = {22607549},
shorttitle = {NetMets},
title = { {{NetMets}}: software for quantifying and visualizing errors in biological network segmentation.},
url = {http://www.biomedcentral.com/1471-2105/13/S8/S7},
volume = {13 Suppl 8},
year = {2012}
}
@article{Costa2014,
abstract = {Efforts to map neural circuits from model organisms including flies and mice are now generating multi-terabyte datasets of 10,000s of labelled neurons. Technologies such as dense EM based reconstruction, and sparse/multicolor labeling with image registration allow neurons to be embedded within the spatial context of a circuit or a whole brain. These ever-expanding data demand new computational tools to search, organize and navigate neurons. We present a simple, but fast and sensitive, algorithm, NBLAST, for measuring pairwise neuronal similarity by position and local geometry. Inspired by the BLAST algorithm for biological sequence data, NBLAST decomposes a query and target neuron into short segments. Each matched segment pair is scored using a log-likelihood ratio scoring matrix empirically defined by the statistics of real matches and non-matches in the data. We demonstrate the application of a reference implementation of NBLAST to a dataset of 16,129 single Drosophila neurons. NBLAST scores are sensitive enough to distinguish 1) two images of the same neuron, 2) two neurons of the same identified neuronal type 3) two neurons of very closely related types. We demonstrate that NBLAST scores can be used to identify neuronal types, such as olfactory projection neurons, with reliability that matches or exceeds expert annotation in a fraction of the time. We also show that clustering using appropriately normalized NBLAST scores can reveal classic morphological types as well as identify unpublished classes. We carry out detailed analysis of a number of neuronal classes including Kenyon cells, olfactory and visual projection neurons, auditory, and male-specific P1 neurons. This identifies many new neuronal types and reveals unreported features of topographic organization. Finally we provide a complete clustering of the 16,129 neurons in the test dataset into 1,052 clusters of highly related neurons. These clusters are then organized into superclusters, enabling both exploration of the dataset and the matching of individual clusters to morphological types in the literature. Finally NBLAST queries can be used to identify candidate neurons matching neurite tracts with transgene expression pattern. We provide a general purpose open source toolbox that implements construction of score matrices, the core pairwise scoring algorithm, de novo and precomputed database search and clustering along with complete source code and data for the analyses in this paper. The neuronal families can also be queried online through virtualflybrain.org and visualized in interactive 3D at jefferislab.org.},
author = {Costa, M. and Ostrovsky, A. D. and Manton, J. D. and Prohaska, S. and Jefferis, G. S. X. E.},
doi = {10.1101/006346},
file = {:tmp/006346.full-1.pdf:pdf},
journal = {bio{R}xiv},
pages = {006346},
title = { {{NBLAST}}: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases},
url = {http://biorxiv.org/content/early/2014/08/09/006346.abstract},
year = {2014}
}
@phdthesis{Gillette2015,
abstract = {Neuronal morphology is a key mediator of neuronal function, defining the profile of connectivity and shaping signal integration and propagation. Reconstructing neurite processes is technically challenging and thus data has historically been relatively sparse. Data collection and curation along with more efficient and reliable data production methods provide opportunities for the application of informatics to find new relationships and more effectively explore the field. This dissertation presents a method for aiding the development of data production as well as a novel representation and set of analyses for extracting morphological patterns.},
author = {Gillette, Todd Aaron},
file = {:tmp/Gillette{\_}gmu{\_}0883E{\_}10865.pdf:pdf},
keywords = {Bioinformatics,Nanoscience,motif analysis,neuroinformatics,neuronal morphology,neuronal reconstruction,sequence alignment},
pages = {274},
school = {George Mason University},
title = {Comparative Topological Analysis of Neuronal Arbors via Sequence Representation and Alignment},
type = {Dissertation},
url = {http://digilib.gmu.edu/jspui/handle/1920/9628},
year = {2015}
}
%%}}}
%% Datasets {{{
@article{Duke-Southampton-archive:Cannon:1998,
abstract = {We have developed an on-line archive of neuronal geometry to encourage the use of realistic dendritic structures in morphometry and for neuronal modeling, located at web address www.neuro.soton.ac.uk. Initially we have included full three-dimensional representations of 87 neurons from the hippocampus, obtained following intracellular staining with biocytin and reconstruction using Neurolucida. The archive system includes a structure editor for correcting any departures from valid branching geometry and which allows simple errors in the digitisation to be corrected. The editor employs a platform-independent file format which enforces the constraints that there should be no isolated branches and no closed loops. It also incorporates software for interconversion between the archive format and those used by various neuronal reconstruction and modelling packages. The raw data from digitisation software can be included in the archive as well as edited reconstructions and any further information available. Cross-referenced tables and indexes are updated automatically and are sorted according to a number of fields including the cell type, contributor, submission date and published reference. Both the archive and the structure editor should facilitate the quantitative use of full three-dimensional reconstructions of neurons from the hippocampus and other brain regions.},
author = {Cannon, R. C. and Turner, D. A. and Pyapali, G. K. and Wheal, H. V.},
doi = {10.1016/S0165-0270(98)00091-0},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Cannon et al. - 1998 - An on-line archive of reconstructed hippocampal neurons.pdf:pdf},
isbn = {0165-0270 (Print)},
issn = {01650270},
journal = {Journal of Neuroscience Methods},
keywords = {Compartmental modeling,Morphology,Neuronal geometry,Three-dimensional reconstruction},
number = {1},
pages = {49--54},
pmid = {9821633},
title = {An on-line archive of reconstructed hippocampal neurons},
url = {http://www.sciencedirect.com/science/article/pii/S0165027098000910},
volume = {84},
year = {1998}
}
@article{DIADEM-dataset:Brown:2011,
abstract = {The comprehensive characterization of neuronal morphology
requires tracing extensive axonal and dendritic arbors imaged
with light microscopy into digital reconstructions.
Considerable effort is ongoing to automate this greatly
labor-intensive and currently rate-determining process.
Experimental data in the form of manually traced
digital reconstructions and corresponding image stacks
play a vital role in developing increasingly more
powerful reconstruction algorithms. The DIADEM
challenge (short for DIgital reconstruction of Axonal
and DEndritic Morphology) successfully
stimulated progress in this area by utilizing six data
set collections from different animal species, brain
regions, neuron types, and visualization methods. The
original research projects that provided these data are
representative of the diverse scientific questions
addressed in this field. At the same time, these data
provide a benchmark for the types of demands automated
software must meet to achieve the quality of manual
reconstructions while minimizing human involvement. The
DIADEM data underwent extensive curation, including
quality control, metadata annotation, and format
standardization, to focus the challenge on the most
substantial technical obstacles. This data set package
is now freely released (http://diademchallenge.org)
to train, test, and aid development of automated
reconstruction algorithms.},
author = {Brown, Kerry M and Barrionuevo, Germ\'{a}n and Canty, Alison J and {De Paola}, Vincenzo and Hirsch, Judith a and Jefferis, Gregory S X E and Lu, Ju and Snippe, Marjolein and Sugihara, Izumi and Ascoli, Giorgio a},
doi = {10.1007/s12021-010-9095-5},
file = {:home/zaki/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Brown et al. - 2011 - The DIADEM data sets representative light microscopy images of neuronal morphology to advance automation of digita.pdf:pdf},
issn = {1559-0089},
journal = {Neuroinformatics},
keywords = {Animals,Axons,Axons: physiology,Axons: ultrastructure,Humans,Image Processing, Computer-Assisted,Image Processing, Computer-Assisted: methods,Image Processing, Computer-Assisted: trends,Microscopy,Microscopy: methods,Microscopy: trends,Neuroanatomical Tract-Tracing Techniques,Neuroanatomical Tract-Tracing Techniques: methods,Neuroanatomical Tract-Tracing Techniques: trends,Neurons,Neurons: cytology,Neurons: physiology,Software Design},
month = {sep},
number = {2-3},
pages = {143--57},
pmid = {21249531},
title = {The {DIADEM} data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21249531},
volume = {9},
year = {2011}
}
%%}}}
%% Software {{{
@book{MATLAB:2013a,
year = {2013},
author = {MATLAB},
title = {version 8.1 (R2013a)},
publisher = {The MathWorks Inc.},
address = {Natick, Massachusetts}
}
@online{MATLAB:fdep,
title = { {{fdep}}: a pedestrian function dependencies finder},
author = {Schwarz, Urs},
date = {2010-06-20},
url = {http://www.mathworks.com/matlabcentral/fileexchange/17291-fdep--a-pedestrian-function-dependencies-finder},
urldate = {2015-07-08}
}
@article{GraphViz:Gansner:2000,
author = {Gansner, Emden R. and North, Stephen C.},
title = {An Open Graph Visualization System and Its Applications to Software Engineering},
journal = {Softw. Pract. Exper.},
issue_date = {Sept. 2000},
volume = {30},
number = {11},
month = sep,
year = {2000},
issn = {0038-0644},
pages = {1203--1233},
numpages = {31},
url = {http://dx.doi.org.ezproxy.lib.uh.edu/10.1002/1097-024X(200009)30:11<1203::AID-SPE338>3.3.CO;2-E},
doi = {10.1002/1097-024X(200009)30:11<1203::AID-SPE338>3.3.CO;2-E},
acmid = {358697},
publisher = {John Wiley \& Sons, Inc.},
address = {New York, NY, USA},
keywords = {graph visualization, open systems, software engineering},
}
% 10.3389/fninf.2012.00022
@article{NeuroDebian:Halchenko:2012,
author={Halchenko, Yaroslav O. and Hanke, Michael},
title={Open is not enough. Let's take the next step: An integrated, community-driven computing platform for neuroscience},
journal={Frontiers in Neuroinformatics},
volume={6},
year={2012},
number={22},
url={http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2012.00022/full},
doi={10.3389/fninf.2012.00022},
issn={1662-5196}
}
@online{Software-rewrites:Spolsky:2000,
title = {{J}oel on Software: Things You Should Never Do, part {I}},
author = {Spolsky, Joel},
year = 2000,
month = {apr},
date = {2000-04-06},
url = {http://www.joelonsoftware.com/articles/fog0000000069.html},
urldate = {2015-08-04}
}
@online{IRM-SDLC:DoJ:2003,
title = {The {D}epartment of {J}ustice Systems Development Life Cycle Guidance Document},
author = {{Justice Management Division}},
publisher = {{Department of Justice}},
year = 2003,
month = {jan},
url = {http://www.justice.gov/archive/jmd/irm/lifecycle/table.htm},
urldate = {2015-11-10}
}
@article{Schneider2012,
abstract = {For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.},
author = {Schneider, Caroline A and Rasband, Wayne S and Eliceiri, Kevin W},
doi = {10.1038/nmeth.2089},
file = {:home/zaki/Downloads/nmeth.2089.pdf:pdf},
isbn = {1548-7091},
issn = {1548-7091},
journal = {Nature Methods},
number = {7},
pages = {671--675},
pmid = {22930834},
publisher = {Nature Publishing Group},
title = { {{NIH Image}} to {ImageJ}: 25 years of image analysis},
url = {http://dx.doi.org/10.1038/nmeth.2089},
volume = {9},
year = {2012}
}
@online{CppCon:Hourglass:2014,
title = {Hourglass Interfaces for {C++} {API}s},
publisher = {CppCon 2014},
author = {Du Toit, Stefanus},
date = {2014-09-10},
year = 2014,
url = {http://www.slideshare.net/StefanusDuToit/cpp-con-2014-hourglass-interfaces-for-c-apis},
urldate = {2015-11-22}
}
@online{anderson2000end,
title={The end of {DLL} hell},
publisher = {MSDN},
author={Anderson, Rick},
url = {http://web.archive.org/web/20010605023737/http://msdn.microsoft.com/library/techart/dlldanger1.htm},
year={2000}
}
@online{ModellingSoftDep:Burrows,
title={Modelling and Resolving Software Dependencies},
author={Burrows, Daniel},
date = {2005-06-15},
url = {https://people.debian.org/~dburrows/model.pdf},
year={2005},
pages = {1--16}
}
@inproceedings{Guo2011,
abstract = {It can be painfully hard to take software that runs on one person's machine and get it to run on another machine. Online forums and mailing lists are filled with discussions of users' troubles with compiling, installing, and configuring software and ...},
author = {Guo, Philip J. and Engler, Dawson},
file = {:tmp/GuoEngler.pdf:pdf},
x_booktitle = {USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference},
booktitle = {Proc. USENIX Annual Technical Conference},
x_series = {USENIX'11},
location = {Portland, OR},
x_publisher = {USENIX Association},
address = {Berkeley, CA, USA},
pages = {21},
title = { {{CDE}}: Using System Call Interposition to Automatically Create Portable Software Packages},
url = {http://dl.acm.org/citation.cfm?id=2002181.2002202},
acmid = {2002202},
year = {2011}
}
%%}}}
%% Quotes {{{
% Baltimore:2001:BBI:504949.504953
@incollection{Baltimore:2001,
author = {Baltimore, David},
chapter = {How Biology Became an Information Science},
booktitle = {The Invisible Future: The Seamless Integration of Technology into Everyday Life},
editor = {Denning, Peter J.},
year = {2001},
isbn = {0-07-138224-0},
pages = {43--55},
numpages = {13},
url = {http://dl.acm.org/citation.cfm?id=504949.504953},
acmid = {504953},
publisher = {McGraw-Hill, Inc.},
address = {New York, NY, USA},
}
% Denning:2001:IFS:504949
@book{Denning:2001,
editor = {Denning, Peter J.},
title = {The Invisible Future: The Seamless Integration of Technology into Everyday Life},
year = {2001},
isbn = {0-07-138224-0},
url = {http://dl.acm.org/citation.cfm?id=504949},
publisher = {McGraw-Hill, Inc.},
address = {New York, NY, USA},
}
@online{ProgrammersAlphabet,
title = {The Programmer's Alphabet},
author = {Savitzky, Stephen},
year = 1981,
url = {http://steve.savitzky.net/Songs/alphabet/},
urldate = {2015-11-20}
}
%%}}}
%% BigNeuron {{{
@article{BigNeuron:Peng:2015,
title={{B}ig{N}euron: Large-Scale {3D} Neuron Reconstruction from Optical Microscopy Images},
volume={87},
ISSN={0896-6273},
url={http://dx.doi.org/10.1016/j.neuron.2015.06.036},
DOI={10.1016/j.neuron.2015.06.036},
number={2},
journal={Neuron},
publisher={Elsevier BV},
author={Peng, Hanchuan and Hawrylycz, Michael and Roskams, Jane and Hill, Sean and Spruston, Nelson and Meijering, Erik and Ascoli, Giorgio A.},
year={2015},
month = {jul},
pages={252--256}
}
@article{Vaa3D:Peng:2010,
title={{V3D} enables real-time {3D} visualization and quantitative analysis of large-scale biological image data sets},
volume={28},
ISSN={1546-1696},
url={http://dx.doi.org/10.1038/nbt.1612},
DOI={10.1038/nbt.1612},
number={4},
journal={Nature Biotechnology},
publisher={Nature Publishing Group},
author={Peng, Hanchuan and Ruan, Zongcai and Long, Fuhui and Simpson, Julie H and Myers, Eugene W},
year={2010},
month = {mar},
pages={348--353}
}
@article{Vaa3D:Peng:2014,
title={Extensible visualization and analysis for multidimensional images using {Vaa3D}},
volume={9},
ISSN={1750-2799},
url={http://dx.doi.org/10.1038/nprot.2014.011},
DOI={10.1038/nprot.2014.011},
number={1},
journal={Nature Protocols},
publisher={Nature Publishing Group},
author={Peng, Hanchuan and Bria, Alessandro and Zhou, Zhi and Iannello, Giulio and Long, Fuhui},
year={2014},
month = {jan},
pages={193--208}
}
@article{DIADEM2BigNeuron:Peng:2015,
title = {From {DIADEM} to {B}ig{N}euron},
volume = {13},
ISSN = {1559-0089},
url = {http://dx.doi.org/10.1007/s12021-015-9270-9},
DOI = {10.1007/s12021-015-9270-9},
number = {3},
journal = {Neuroinformatics},
publisher = {Springer Science + Business Media},
author = {Peng, Hanchuan and Meijering, Erik and Ascoli, Giorgio A.},
year = {2015},
month = {apr},
pages = {259--260}
}
@online{BigNeuron:FAQ:2015,
title = {Frequently Asked Questions},
author = {{BigNeuron Consortium}},
year = 2015,
month = {mar},
date = {2015-03-13},
url = {https://alleninstitute.org/bigneuron/faq/},
urldate = {2015-08-07}
}
@online{Vaa3D:site:2015,
title = {{V}aa{3D}: {O}pen-Source, Multi-dimensional Data Visualization and Analysis},
author = {{Hanchuan Peng Group}},
year = 2015,
month = {may},
date = {2015-03-30},
url = {http://www.vaa3d.org/},
urldate = {2015-08-07}
}
%%}}}
%% Reviews {{{
@article{NeuroTracePerspect:Meijering:2010,
title={Neuron tracing in perspective},
volume={77A},
ISSN={1552-4930},
url={http://dx.doi.org/10.1002/cyto.a.20895},
DOI={10.1002/cyto.a.20895},
number={7},
journal={Cytometry Part A},
publisher={Wiley-Blackwell},
author={Meijering, Erik},
year={2010},
month = {mar},
pages={693--704}
}
@article{NeuroMorphTrends:Halavi:2012,
title={Digital Reconstructions of Neuronal Morphology: Three Decades of Research Trends},
volume={6},
ISSN={1662-4548},
url={http://dx.doi.org/10.3389/fnins.2012.00049},
DOI={10.3389/fnins.2012.00049},
journal={Frontiers in Neuroscience},
publisher={Frontiers Media SA},
author={Halavi, Maryam and Hamilton, Kelly A. and Parekh, Ruchi and Ascoli, Giorgio A.},
year={2012}
}
@article{DIADEM&Beyond:Liu:2011,
title={The {DIADEM} and Beyond},
volume={9},
ISSN={1559-0089},
url={http://dx.doi.org/10.1007/s12021-011-9102-5},
DOI={10.1007/s12021-011-9102-5},
number={2-3},
journal={Neuroinformatics},
publisher={Springer Science + Business Media},
author={Liu, Yuan},
year={2011},
month = {mar},
pages={99--102}
}
@article{Halavi2012,
abstract = {The importance of neuronal morphology has been recognized from the early days of neuroscience. Elucidating the functional roles of axonal and dendritic arbors in synaptic integration, signal transmission, network connectivity, and circuit dynamics requires quantitative analyses of digital three-dimensional reconstructions. We extensively searched the scientific literature for all original reports describing reconstructions of neuronal morphology since the advent of this technique three decades ago. From almost 50,000 titles, 30,000 abstracts, and more than 10,000 full-text articles, we identified 902 publications describing ∼44,000 digital reconstructions. Reviewing the growth of this field exposed general research trends on specific animal species, brain regions, neuron types, and experimental approaches. The entire bibliography, annotated with relevant metadata and (wherever available) direct links to the underlying digital data, is accessible at NeuroMorpho.Org.},
author = {Halavi, Maryam and Hamilton, Kelly a. and Parekh, Ruchi and Ascoli, Giorgio a.},
doi = {10.3389/fnins.2012.00049},
file = {:tmp/fnins-06-00049-2.pdf:pdf},
issn = {16624548},
journal = {Frontiers in Neuroscience},
keywords = {Data mining,Data sharing,Digital reconstruction,Literature mining,NeuroMorpho.Org,Neuron morphology,Potential connectivity,Three-dimensional reconstruction},
number = {apr},
pages = {1--11},
pmid = {22536169},
title = {Digital reconstructions of neuronal morphology: Three decades of research trends},
volume = {6},
year = {2012}
}
@article{Helmstaedter2012,
abstract = {The connectivity architecture of neuronal circuits is essential to understand how brains work, yet our knowledge about the neuronal wiring diagrams remains limited and partial. Technical breakthroughs in labeling and imaging methods starting more than a century ago have advanced knowledge in the field. However, the volume of data associated with imaging a whole brain or a significant fraction thereof, with electron or light microscopy, has only recently become amenable to digital storage and analysis. A mouse brain imaged at light-microscopic resolution is about a terabyte of data, and 1mm $\backslash$n 3 of the brain at EM resolution is about half a petabyte. This has given rise to a new field of research, computational analysis of large-scale neuroanatomical data sets, with goals that include reconstructions of the morphology of individual neurons as well as entire circuits. The problems encountered include large data management, segmentation and 3D reconstruction, computational geometry and workflow management allowing for hybrid approaches combining manual and algorithmic processing. Here we review this growing field of neuronal data analysis with emphasis on reconstructing neurons from EM data cubes. ?? 2012.},
author = {Helmstaedter, Moritz and Mitra, Partha P.},
doi = {10.1016/j.conb.2011.11.010},
file = {:tmp/1-s2.0-S0959438811002133-main.pdf:pdf},
isbn = {1873-6882 (Electronic)$\backslash$r0959-4388 (Linking)},
issn = {09594388},
journal = {Current Opinion in Neurobiology},
number = {1},
pages = {162--169},
pmid = {22221862},
publisher = {Elsevier Ltd},
title = {Computational methods and challenges for large-scale circuit mapping},
url = {http://dx.doi.org/10.1016/j.conb.2011.11.010},
volume = {22},
year = {2012}
}
%%}}}
%% Access to better data {{{
@article{NeuroMorphVariability:Parekh:2015,
abstract = {Digital reconstructions of axonal and dendritic arbors provide a powerful representation of neuronal morphology in formats amenable to quantitative analysis, computational modeling, and data mining. Reconstructed files, however, require adequate metadata to identify the appropriate animal species, developmental stage, brain region, and neuron type. Moreover, experimental details about tissue processing, neurite visualization and microscopic imaging are essential to assess the information content of digital morphologies. Typical morphological reconstructions only partially capture the underlying biological reality. Tracings are often limited to certain domains (e.g., dendrites and not axons), may be incomplete due to tissue sectioning, imperfect staining, and limited imaging resolution, or can disregard aspects irrelevant to their specific scientific focus (such as branch thickness or depth). Gauging these factors is critical in subsequent data reuse and comparison. NeuroMorpho.Org is a central repository of reconstructions from many laboratories and experimental conditions. Here, we introduce substantial additions to the existing metadata annotation aimed to describe the completeness of the reconstructed neurons in NeuroMorpho.Org. These expanded metadata form a suitable basis for effective description of neuromorphological data.},
title={The importance of metadata to assess information content in digital reconstructions of neuronal morphology},
volume={360},
ISSN={1432-0878},
url={http://dx.doi.org/10.1007/s00441-014-2103-6},
DOI={10.1007/s00441-014-2103-6},
number={1},
journal={Cell and Tissue Research},
publisher={Springer Science + Business Media},
author = {Parekh, Ruchi and Arma{\~{n}}anzas, Rub{\'{e}}n and Ascoli, Giorgio a.},
year={2015},
month = {feb},
pages={121--127},
pmid = {25653123},
}
@article{Poldrack2011,
abstract = {Neuron, 72 (2011) 692-697. doi:10.1016/j.neuron.2011.11.001},
author = {Poldrack, Russell A},
doi = {10.1016/j.neuron.2011.11.001},
file = {:tmp/1-s2.0-S0896627311009895-main.pdf:pdf},
isbn = {0896-6273},
issn = {1097-4199},
journal = {Neuron},
number = {5},
pages = {692--697},
pmid = {22153367},
publisher = {Elsevier Inc.},
title = {Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding},
url = {http://dx.doi.org/10.1016/j.neuron.2011.11.001},
volume = {72},
year = {2011}
}
%%}}}
%% neuroscience projects {{{
@article{Kennedy2016,
abstract = {The Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC - www.nitrc.org) suite of services include a resources registry, image repository and a cloud computational environment to meet the needs of the neuroimaging researcher. NITRC provides image-sharing functionality through both the NITRC Resource Registry (NITRC-R), where bulk data files can be released through the file release system (FRS), and the NITRC Image Repository (NITRC-IR), a XNAT-based image data management system. Currently hosting 14 projects, 6845 subjects, and 8285 MRI imaging sessions, NITRC-IR provides a large array of structural, diffusion and resting state MRI data. Designed to be flexible about management of data access policy, NITRC provides a simple, free, NIH-funded service to support resource sharing in general, and image sharing in particular.},
author = {Kennedy, David N and Haselgrove, Christian and Riehl, Jon and Preuss, Nina and Buccigrossi, Robert},
doi = {10.1016/j.neuroimage.2015.05.074},
file = {:tmp/1-s2.0-S1053811915004644-main.pdf:pdf},
issn = {1095-9572},
journal = {NeuroImage},
pages = {1069--1073},
pmid = {26044860},
publisher = {Elsevier Inc.},
title = {The {NITRC} image repository.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26044860},
volume = {124, Part B},
year = {2016},
month = {jan}
}
@article{Gardner2008,
author = {Gardner, Daniel and Akil, Huda and Ascoli, Giorgio A. and Bowden, Douglas M and Bug, William and Donohue, Duncan E. and Goldberg, David H. and Grafstein, Bernice and Grethe, Jeffrey S. and Gupta, Amarnath and Halavi, Maryam and Kennedy, David N. and Marenco, Luis and Martone, Maryann E. and Miller, Perry L. and M{\"{u}}ller, Hans-Michael and Robert, Adrian and Shepherd, Gordon M. and Sternberg, Paul W. and {Van Essen}, David C. and Williams, Robert W.},
doi = {10.1007/s12021-008-9024-z},
file = {:tmp/nihms94457.pdf:pdf},
issn = {1539-2791},
journal = {Neuroinformatics},
month = {sep},
number = {3},
pages = {149--160},
title = {The {N}euroscience {I}nformation {F}ramework: A Data and Knowledge Environment for Neuroscience},
url = {http://link.springer.com/10.1007/s12021-008-9024-z},
volume = {6},
year = {2008}
}
@article{Ascoli2007,
author = {Ascoli, G. A. and Donohue, D. E. and Halavi, M.},
doi = {10.1523/JNEUROSCI.2055-07.2007},
file = {:tmp/9247.full.pdf:pdf},
isbn = {2712005775},
issn = {0270-6474},
journal = {Journal of Neuroscience},
number = {35},
pages = {9247--9251},
title = { {{NeuroMorpho.Org}}: A Central Resource for Neuronal Morphologies},
url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.2055-07.2007},
volume = {27},
year = {2007}
}
@article{Helmstaedter2011,
author = {Helmstaedter, Moritz and Briggman, Kevin L and Denk, Winfried},
doi = {10.1038/nn.2868},
file = {:tmp/nn.2868.pdf:pdf},
issn = {1097-6256},
journal = {Nature Neuroscience},
number = {8},
pages = {1081--1088},
publisher = {Nature Publishing Group},
title = {High-accuracy neurite reconstruction for high-throughput neuroanatomy},
url = {http://www.nature.com/doifinder/10.1038/nn.2868},
volume = {14},