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tustisonSection.bib
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%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/
%% Created for Nick Tustison at 2013-10-09 15:01:02 -0400
%% Saved with string encoding Unicode (UTF-8)
@article{ince2012,
Abstract = {Scientific communication relies on evidence that cannot be entirely included in publications, but the rise of computational science has added a new layer of inaccessibility. Although it is now accepted that data should be made available on request, the current regulations regarding the availability of software are inconsistent. We argue that, with some exceptions, anything less than the release of source programs is intolerable for results that depend on computation. The vagaries of hardware, software and natural language will always ensure that exact reproducibility remains uncertain, but withholding code increases the chances that efforts to reproduce results will fail.},
Author = {Ince, Darrel C and Hatton, Leslie and Graham-Cumming, John},
Date-Added = {2013-10-09 19:00:53 +0000},
Date-Modified = {2013-10-09 19:01:02 +0000},
Doi = {10.1038/nature10836},
Journal = {Nature},
Journal-Full = {Nature},
Mesh = {Algorithms; Editorial Policies; Information Dissemination; Intellectual Property; Periodicals as Topic; Publishing; Reproducibility of Results; Research; Research Design; Software},
Month = {Feb},
Number = {7386},
Pages = {485-8},
Pmid = {22358837},
Pst = {epublish},
Title = {The case for open computer programs},
Volume = {482},
Year = {2012},
Bdsk-Url-1 = {http://dx.doi.org/10.1038/nature10836}}
@article{avants2011a,
Abstract = {The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669$\pm$0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.},
Author = {Avants, Brian B and Tustison, Nicholas J and Song, Gang and Cook, Philip A and Klein, Arno and Gee, James C},
Date-Added = {2013-07-10 13:23:20 +0000},
Date-Modified = {2013-07-10 13:24:20 +0000},
Doi = {10.1016/j.neuroimage.2010.09.025},
Journal = {Neuroimage},
Journal-Full = {NeuroImage},
Mesh = {Algorithms; Brain; Databases, Factual; Diagnostic Imaging; Head; Humans; Image Processing, Computer-Assisted; Linear Models; Models, Anatomic; Models, Neurological; Population; Reproducibility of Results; Software},
Month = {Feb},
Number = {3},
Pages = {2033-44},
Pmc = {PMC3065962},
Pmid = {20851191},
Pst = {ppublish},
Title = {A reproducible evaluation of {ANTs} similarity metric performance in brain image registration},
Volume = {54},
Year = {2011},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2010.09.025}}
@article{tustison2010,
Abstract = {A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.},
Author = {Tustison, Nicholas J and Avants, Brian B and Cook, Philip A and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A and Gee, James C},
Date-Added = {2013-03-24 03:42:39 +0000},
Date-Modified = {2013-04-09 22:21:44 +0000},
Doi = {10.1109/TMI.2010.2046908},
Journal = {IEEE Trans Med Imaging},
Journal-Full = {IEEE transactions on medical imaging},
Mesh = {Algorithms; Artifacts; Brain; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Reproducibility of Results; Sensitivity and Specificity},
Month = {Jun},
Number = {6},
Pages = {1310-20},
Pmc = {PMC3071855},
Pmid = {20378467},
Pst = {ppublish},
Title = {{N4ITK}: improved {N3} bias correction},
Volume = {29},
Year = {2010},
Bdsk-Url-1 = {http://dx.doi.org/10.1109/TMI.2010.2046908}}
@article{avants2011,
Abstract = {We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs ( http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.},
Author = {Avants, Brian B and Tustison, Nicholas J and Wu, Jue and Cook, Philip A and Gee, James C},
Date-Added = {2013-03-24 01:22:58 +0000},
Date-Modified = {2013-03-24 01:22:58 +0000},
Doi = {10.1007/s12021-011-9109-y},
Journal = {Neuroinformatics},
Journal-Full = {Neuroinformatics},
Mesh = {Access to Information; Algorithms; Bayes Theorem; Databases, Factual; Humans; Image Processing, Computer-Assisted; Internet; Magnetic Resonance Imaging; Models, Statistical; Pattern Recognition, Automated; Software},
Month = {Dec},
Number = {4},
Pages = {381-400},
Pmc = {PMC3297199},
Pmid = {21373993},
Pst = {ppublish},
Title = {An open source multivariate framework for n-tissue segmentation with evaluation on public data},
Volume = {9},
Year = {2011},
Bdsk-Url-1 = {http://dx.doi.org/10.1007/s12021-011-9109-y}}
@article{avants2010,
Abstract = {We evaluate the impact of template choice on template-based segmentation of the hippocampus in epilepsy. Four dataset-specific strategies are quantitatively contrasted: the "closest to average" individual template, the average shape version of the closest to average template, a best appearance template and the best appearance and shape template proposed here and implemented in the open source toolkit Advanced Normalization Tools (ANTS). The cross-correlation similarity metric drives the correspondence model and is used consistently to determine the optimal appearance. Minimum shape distance in the diffeomorphic space determines optimal shape. Our evaluation results show that, with respect to gold-standard manual labeling of hippocampi in epilepsy, optimal shape and appearance template construction outperforms the other strategies for gaining data-derived templates. Our results also show the improvement is most significant on the diseased side and insignificant on the healthy side. Thus, the importance of the template increases when used to study pathology and may be less critical for normal control studies. Furthermore, explicit geometric optimization of the shape component of the unbiased template positively impacts the study of diseased hippocampi.},
Author = {Avants, Brian B and Yushkevich, Paul and Pluta, John and Minkoff, David and Korczykowski, Marc and Detre, John and Gee, James C},
Date-Added = {2013-03-22 16:44:28 +0000},
Date-Modified = {2013-03-22 16:44:28 +0000},
Doi = {10.1016/j.neuroimage.2009.09.062},
Journal = {Neuroimage},
Journal-Full = {NeuroImage},
Mesh = {Algorithms; Atlases as Topic; Epilepsy; Hippocampus; Humans; Image Interpretation, Computer-Assisted},
Month = {Feb},
Number = {3},
Pages = {2457-66},
Pmc = {PMC2818274},
Pmid = {19818860},
Pst = {ppublish},
Title = {The optimal template effect in hippocampus studies of diseased populations},
Volume = {49},
Year = {2010},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2009.09.062}}
@article{landman2011,
Abstract = {Modern MRI image processing methods have yielded quantitative, morphometric, functional, and structural assessments of the human brain. These analyses typically exploit carefully optimized protocols for specific imaging targets. Algorithm investigators have several excellent public data resources to use to test, develop, and optimize their methods. Recently, there has been an increasing focus on combining MRI protocols in multi-parametric studies. Notably, these have included innovative approaches for fusing connectivity inferences with functional and/or anatomical characterizations. Yet, validation of the reproducibility of these interesting and novel methods has been severely hampered by the limited availability of appropriate multi-parametric data. We present an imaging protocol optimized to include state-of-the-art assessment of brain function, structure, micro-architecture, and quantitative parameters within a clinically feasible 60-min protocol on a 3-T MRI scanner. We present scan-rescan reproducibility of these imaging contrasts based on 21 healthy volunteers (11 M/10 F, 22-61 years old). The cortical gray matter, cortical white matter, ventricular cerebrospinal fluid, thalamus, putamen, caudate, cerebellar gray matter, cerebellar white matter, and brainstem were identified with mean volume-wise reproducibility of 3.5%. We tabulate the mean intensity, variability, and reproducibility of each contrast in a region of interest approach, which is essential for prospective study planning and retrospective power analysis considerations. Anatomy was highly consistent on structural acquisition (~1-5% variability), while variation on diffusion and several other quantitative scans was higher (~<10%). Some sequences are particularly variable in specific structures (ASL exhibited variation of 28% in the cerebral white matter) or in thin structures (quantitative T2 varied by up to 73% in the caudate) due, in large part, to variability in automated ROI placement. The richness of the joint distribution of intensities across imaging methods can be best assessed within the context of a particular analysis approach as opposed to a summary table. As such, all imaging data and analysis routines have been made publicly and freely available. This effort provides the neuroimaging community with a resource for optimization of algorithms that exploit the diversity of modern MRI modalities. Additionally, it establishes a baseline for continuing development and optimization of multi-parametric imaging protocols.},
Author = {Landman, Bennett A and Huang, Alan J and Gifford, Aliya and Vikram, Deepti S and Lim, Issel Anne L and Farrell, Jonathan A D and Bogovic, John A and Hua, Jun and Chen, Min and Jarso, Samson and Smith, Seth A and Joel, Suresh and Mori, Susumu and Pekar, James J and Barker, Peter B and Prince, Jerry L and van Zijl, Peter C M},
Date-Added = {2013-03-22 14:40:43 +0000},
Date-Modified = {2013-04-09 22:21:07 +0000},
Doi = {10.1016/j.neuroimage.2010.11.047},
Journal = {Neuroimage},
Journal-Full = {NeuroImage},
Mesh = {Adult; Brain; Brain Mapping; Female; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Reproducibility of Results; Young Adult},
Month = {Feb},
Number = {4},
Pages = {2854-66},
Pmc = {PMC3020263},
Pmid = {21094686},
Pst = {ppublish},
Title = {Multi-parametric neuroimaging reproducibility: a 3-{T} resource study},
Volume = {54},
Year = {2011},
Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.neuroimage.2010.11.047}}