text:
-resolution {CT} images.},
  Author                   = {Schoenemann, P. T. and Monge, J. and Avants, B. B. and Glotzer, D. and Gee, J. C.},
  Journal                  = {Am. J. Phys. Anthropol.},
  Year                     = {2007},
  Number                   = {44},
  Pages                    = {209},

  ISSN                     = {0002-9483},
  Unique-id                = {ISI:000244656500644}
}

@Article{Schoenemann2010,
  Title                    = {Creating statistical atlases of modern primate endocranial morphology rising non-rigid deformation analysis of high-resolution {CT} images.},
  Author                   = {Schoenemann, P. Thomas and Monge, Janet and Holloway, Ralph L. and Avants, Brian B. and Gee, James C.},
  Journal                  = {Am. J. Phys. Anthropol.},
  Year                     = {2010},
  Note                     = {79th Annual Meeting of the American-Association-of-Physical-Anthropologists, Albuquerque, NM, APR 14-17, 2010},
  Number                   = {50},
  Pages                    = {208--209},

  ISSN                     = {0002-9483},
  Organization             = {Amer Assoc Phys Anthropol},
  Unique-id                = {ISI:000275295200695}
}

@Article{Simon2008a,
  Title                    = {Atypical cortical connectivity and visuospatial cognitive impairments are related in children with chromosome 22q11.2 deletion syndrome.},
  Author                   = {Simon, Tony J. and Wu, Zhongle and Avants, Brian and Zhang, Hui and Gee, James C. and Stebbins, Glenn T.},
  Journal                  = {Behav Brain Funct},
  Year                     = {2008},
  Pages                    = {25},
  Volume                   = {4},

  Abstract                 = {Chromosome 22q11.2 deletion syndrome is one of the most common genetic causes of cognitive impairment and developmental disability yet little is known about the neural bases of those challenges. Here we expand upon our previous neurocognitive studies by specifically investigating the hypothesis that changes in neural connectivity relate to cognitive impairment in children with the disorder.Whole brain analyses of multiple measures computed from diffusion tensor image data acquired from the brains of children with the disorder and typically developing controls. We also correlated diffusion tensor data with performance on a visuospatial cognitive task that taps spatial attention.Analyses revealed four common clusters, in the parietal and frontal lobes, that showed complementary patterns of connectivity in children with the deletion and typical controls. We interpreted these results as indicating differences in connective complexity to adjoining cortical regions that are critical to the cognitive functions in which affected children show impairments. Strong, and similarly opposing patterns of correlations between diffusion values in those clusters and spatial attention performance measures considerably strengthened that interpretation.Our results suggest that atypical development of connective patterns in the brains of children with chromosome 22q11.2 deletion syndrome indicate a neuropathology that is related to the visuospatial cognitive impairments that are commonly found in affected individuals.},
  Doi                      = {10.1186/1744-9081-4-25},
  Institution              = {M,I,N,D, Institute, University of California, Davis, 2825 50th Street, Sacramento, CA 95817, USA. tjsimon@ucdavis.edu.},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {stnava},
  Pii                      = {1744-9081-4-25},
  Pmid                     = {18559106},
  Timestamp                = {2013.05.29}
}

@Article{Song2013,
  Title                    = {Using region trajectories to construct an accurate and efficient polyaffine transform model.},
  Author                   = {Song, Gang and Liu, Yang and Wu, Baohua and Avants, Brian and Gee, James C.},
  Journal                  = {Inf Process Med Imaging},
  Year                     = {2013},
  Pages                    = {668--679},
  Volume                   = {23},

  __markedentry            = {[stnava:1]},
  Abstract                 = {In this paper we propose a novel way to construct a diffeomorphic polyaffine model. Each affine transform is defined on a local region and the resulting diffeomorphism encapsulates all the local transforms by a smooth and invertible displacement field. Compared with traditional weighting schemes used in combining local transforms, our new scheme guarantees that the resulting transform precisely preserves the value of each local affine transform. By introducing the trajectory of local regions instead of using regions themselves, the new approach encodes precisely each local affine transform using a diffeomorphism with one or more stationary velocity fields. Experiments show that our new polyaffine model is both accurate and efficient.},
  Keywords                 = {Algorithms; Computer Simulation; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Models, Biological; Models, Statistical; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {24684008},
  Timestamp                = {2014.08.26}
}

@Article{Song2006a,
  Title                    = {Integrated graph cuts for brain {MRI} segmentation.},
  Author                   = {Song, Zhuang and Tustison, Nicholas and Avants, Brian and Gee, James C.},
  Journal                  = {Med Image Comput Comput Assist Interv},
  Year                     = {2006},
  Number                   = {Pt 2},
  Pages                    = {831--838},
  Volume                   = {9},

  __markedentry            = {[stnava:1]},
  Abstract                 = {Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Furthermore, inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneity estimation. In the validation experiments of simulated brain MRIs, the proposed method outperformed a segmentation method based on iterated conditional modes (ICM), which is a commonly used optimization method in medical image segmentation. In the experiments of real neonatal brain MRIs, the results of the proposed method have good overlap with the manual segmentations by human experts.},
  Institution              = {Penn Image Computing and Science Lab, University of Pennsylvania, USA. songz@seas.upenn.edu},
  Keywords                 = {Algorithms; Artificial Intelligence; Brain, anatomy /\&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {17354850},
  Timestamp                = {2013.05.29}
}

@Article{Sun2008,
  Title                    = {Cardiac medial modeling and time-course heart wall thickness analysis.},
  Author                   = {Sun, Hui and Avants, Brian B. and Frangi, Alejandro F. and Sukno, Federico and Geel, James C. and Yushkevich, Paul A.},
  Journal                  = {Med Image Comput Comput Assist Interv},
  Year                     = {2008},
  Number                   = {Pt 2},
  Pages                    = {766--773},
  Volume                   = {11},

  __markedentry            = {[stnava:1]},
  Abstract                 = {The medial model is a powerful shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the boundary geometry according to medial geometry. It has been recently extended to model complex shapes with multi-figures, i.e., shapes whose skeletons can not be described by a single sheet in 3D. This paper applied the medial model to a 2-chamber heart data set consisting of 428 cardiac shapes from 90 subjects. The results show that the medial model can capture the heart shape accurately. To demonstrate the usage of the medial model, the changes of the heart wall thickness over time are analyzed. We calculated the mean heart wall thickness map of 90 subjects for different phases of the cardiac cycle, as well as the mean thickness change between phases.},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.},
  Keywords                 = {Algorithms; Computer Simulation; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Magnetic Resonance Imaging, methods; Models, Cardiovascular; Myocardium, pathology; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {18982674},
  Timestamp                = {2013.05.29}
}

@Article{Sundaram2005,
  Title                    = {Towards a dynamic model of pulmonary parenchymal deformation: evaluation of methods for temporal reparameterization of lung data.},
  Author                   = {Sundaram, Tessa A. and Avants, Brian B. and Gee, James C.},
  Journal                  = {Med Image Comput Comput Assist Interv},
  Year                     = {2005},
  Number                   = {Pt 2},
  Pages                    = {328--335},
  Volume                   = {8},

  Abstract                 = {We approach the problem of temporal reparameterization of dynamic sequences of lung MR images. In earlier work, we employed capacity-based reparameterization to co-register temporal sequences of 2-D coronal images of the human lungs. Here, we extend that work to the evaluation of a ventilator-acquired 3-D dataset from a normal mouse. Reparameterization according to both deformation and lung volume is evaluated. Both measures provide results that closely approximate normal physiological behavior, as judged from the original data. Our ultimate goal is to be able to characterize normal parenchymal biomechanics over a population of healthy individuals, and to use this statistical model to evaluate lung deformation under various pathological states.},
  Institution              = {University of Pennsylvania, Philadelphia PA 19104, USA.},
  Keywords                 = {Algorithms; Animals; Computer Simulation; Databases, Factual; Elasticity; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Lung, anatomy /\&/ histology/physiology; Magnetic Resonance Imaging, methods; Mice; Models, Biological; Reproducibility of Results; Respiratory Mechanics; Sensitivity and Specificity; Subtraction Technique},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {16685976},
  Timestamp                = {2013.05.29}
}

@InProceedings{Sundaram2004,
  Title                    = {A dynamic model of average lung deformation using capacity-based reparameterization and shape averaging of lung MR images},
  Author                   = {Sundaram, TA and Avants, BB and Gee, JC},
  Booktitle                = {MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2004, PT 2, PROCEEDINGS},
  Year                     = {2004},
  Editor                   = {Barillot, C and Haynor, DR and Hellier, P},
  Note                     = {7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2004), St Malo, FRANCE, SEP 26-29, 2004},
  Number                   = {2},
  Organization             = {IRISA; CNRS; Univ Rennes},
  Pages                    = {1000--1007},
  Series                   = {LECTURE NOTES IN COMPUTER SCIENCE},
  Volume                   = {3217},

  __markedentry            = {[stnava:1]},
  ISBN                     = {3-540-22977-9},
  ISSN                     = {0302-9743},
  Unique-id                = {ISI:000224322400121}
}

@Article{Tustison2011,
  Title                    = {MULTIVARIATE ANALYSIS OF DIFFUSION TENSOR IMAGING {AND} CORTICAL THICKNESS {MAP}S IN A TRAUMATIC BRAIN INJURY (TBI) COHORT USING ADVANCED {NOR}MALIZATION TOOLS (ANTS)},
  Author                   = {Tustison, Nicholas and Avants, Brian and Cook, Philip and Kim, Junghoon and Whyte, John and Gee, James and Ahlers, Stephen and Stone, James},
  Journal                  = {J. Neurotrauma},
  Year                     = {2011},

  Month                    = jun,
  Note                     = {29th Annual National Neurotrauma Symposium, Hollywood Beach, FL, JUL 10-13, 2011},
  Number                   = {6},
  Pages                    = {A111},
  Volume                   = {28},

  ISSN                     = {0897-7151},
  Unique-id                = {ISI:000292457600346}
}

@Article{Tustison2013,
  Title                    = {Explicit B-spline regularization in diffeomorphic image registration.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B.},
  Journal                  = {Front Neuroinform},
  Year                     = {2013},
  Pages                    = {39},
  Volume                   = {7},

  Abstract                 = {Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline "flavored" diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.},
  Doi                      = {10.3389/fninf.2013.00039},
  Institution              = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania Philadelphia, PA, USA.},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {stnava},
  Pmid                     = {24409140},
  Timestamp                = {2014.08.26}
}

@Article{Tustison2012,
  Title                    = {Logical circularity in voxel-based analysis: Normalization strategy may induce statistical bias.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Kim, Junghoon and Whyte, John and Gee, James C. and Stone, James R.},
  Journal                  = {Hum. Brain Mapp.},
  Year                     = {2012},

  Month                    = nov,

  Abstract                 = {Recent discussions within the neuroimaging community have highlighted the problematic presence of selection bias in experimental design. Although initially centering on the selection of voxels during the course of fMRI studies, we demonstrate how this bias can potentially corrupt voxel-based analyses. For such studies, template-based registration plays a critical role in which a representative template serves as the normalized space for group alignment. A standard approach maps each subject's image to a representative template before performing statistical comparisons between different groups. We analytically demonstrate that in these scenarios the popular sum of squared difference (SSD) intensity metric, implicitly surrogating as a quantification of anatomical alignment, instead explicitly maximizes effect size-an experimental design flaw referred to as "circularity bias." We illustrate how this selection bias varies in strength with the similarity metric used during registration under the hypothesis that while SSD-related metrics, such as Demons, will manifest similar effects, other metrics which are not formulated based on absolute intensity differences will produce less of an effect. Consequently, given the variability in voxel-based analysis outcomes with similarity metric choice, we caution researchers specifically in the use of SSD and SSD-related measures where normalization and statistical analysis involve the same image set. Instead, we advocate a more cautious approach where normalization of the individual subject images to the reference space occurs through corresponding image sets which are independent of statistical testing. Alternatively, one can use similarity terms that are less sensitive to this bias. Hum Brain Mapp, 2012. {\copyright} 2012 Wiley Periodicals, Inc.},
  Doi                      = {10.1002/hbm.22211},
  Institution              = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia. ntustison@virginia.edu.},
  Language                 = {eng},
  Medline-pst              = {aheadofprint},
  Owner                    = {stnava},
  Pmid                     = {23151955},
  Timestamp                = {2013.05.29}
}

@Article{Tustison2010,
  Title                    = {N4ITK: improved N3~{b}ias correction.},
  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.},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {2010},

  Month                    = jun,
  Number                   = {6},
  Pages                    = {1310--1320},
  Volume                   = {29},

  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.},
  Doi                      = {10.1109/TMI.2010.2046908},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19140, USA. ntustison@wustl.edu},
  Keywords                 = {Algorithms; Artifacts; Brain, anatomy /&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {20378467},
  Timestamp                = {2014.08.26}
}

@Article{Tustison2010a,
  Title                    = {N4ITK: improved N3 bias correction.},
  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.},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {2010},

  Month                    = {Jun},
  Number                   = {6},
  Pages                    = {1310--1320},
  Volume                   = {29},

  __markedentry            = {[stnava:6]},
  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.},
  Doi                      = {10.1109/TMI.2010.2046908},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19140, USA. ntustison@wustl.edu},
  Keywords                 = {Algorithms; Artifacts; Brain, anatomy /&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {20378467},
  Timestamp                = {2015.02.23},
  Url                      = {http://dx.doi.org/10.1109/TMI.2010.2046908}
}

@Article{Tustison2011a,
  Title                    = {Ventilation-Based Segmentation of the Lungs Using Hyperpolarized He-3 {MRI}},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Flors, Lucia and Altes, Talissa A. and de lange, Eduard E. and Mugler, III, John P. and Gee, James C.},
  Journal                  = {J. Magn. Reson. Imaging},
  Year                     = {2011},

  Month                    = oct,
  Number                   = {4},
  Pages                    = {831--841},
  Volume                   = {34},

  Doi                      = {10.1002/jmri.22738},
  ISSN                     = {1053-1807},
  Orcid-numbers            = {Mugler, John/0000-0002-4140-308X},
  Researcherid-numbers     = {Mugler, John/B-9432-2013},
  Unique-id                = {ISI:000295394400012}
}

@Article{Tustison2011c,
  Title                    = {Ventilation-based segmentation of the lungs using hyperpolarized (3)He MRI.},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Flors, Lucia and Altes, Talissa A. and {de Lange}, Eduard E. and Mugler, 3rd, John P and Gee, James C.},
  Journal                  = {J Magn Reson Imaging},
  Year                     = {2011},

  Month                    = {Oct},
  Number                   = {4},
  Pages                    = {831--841},
  Volume                   = {34},

  __markedentry            = {[stnava:6]},
  Abstract                 = {To develop an automated segmentation method to differentiate the ventilated lung volume on (3) He magnetic resonance imaging (MRI).Computational processing (CP) for each subject consisted of the following three essential steps: 1) inhomogeneity bias correction, 2) whole lung segmentation, and 3) subdivision of the lung segmentation into regions of similar ventilation. Evaluation consisted of two comparative analyses: i) comparison of the number of defects scored by two human readers in 43 subjects, and ii) simultaneous truth and performance level estimation (STAPLE) in 18 subjects in which the ventilation defects were manually segmented by four human readers.There was excellent correlation between the number of ventilation defects tabulated by CP and reader #1 (intraclass correlation coefficient [ICC] = 0.86), CP and reader #2 (ICC = 0.85), and between the two readers (ICC = 0.97). The STAPLE results from the second analysis yielded the following sensitivity/specificity numbers: CP (0.898/0.905), radiologist #1 (0.743/0.897), radiologist #2 (0.501/0.985), radiologist #3 (0.898/0.848), and the first author (0.600/0.984).We developed and evaluated an automated method for quantifying the ventilated lung volume on (3) He MRI. The findings strongly indicate that our proposed algorithmic processing may be a reliable, automatic method for quantitating ventilation defects.},
  Doi                      = {10.1002/jmri.22738},
  Institution              = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA. ntustison@virginia.edu},
  Keywords                 = {Administration, Inhalation; Asthma, diagnosis; Automation; Case-Control Studies; Cystic Fibrosis, diagnosis; Female; Helium, diagnostic use; Humans; Image Processing, Computer-Assisted; Lung, pathology; Magnetic Resonance Imaging, methods; Male; Pulmonary Gas Exchange, physiology; Pulmonary Ventilation, physiology; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {21837781},
  Timestamp                = {2015.02.23},
  Url                      = {http://dx.doi.org/10.1002/jmri.22738}
}

@Article{Tustison2009,
  Title                    = {Directly Manipulated Free-Form Deformation Image Registration},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Gee, James C.},
  Journal                  = {IEEE Trans Image Process},
  Year                     = {2009},

  Month                    = mar,
  Number                   = {3},
  Pages                    = {624--635},
  Volume                   = {18},

  Doi                      = {10.1109/TIP.2008.2010072},
  ISSN                     = {1057-7149},
  Unique-id                = {ISI:000263604700014}
}

@Article{Tustison2011b,
  Title                    = {Topological Well-Composedness and Glamorous Glue: A Digital Gluing Algorithm for Topologically Constrained Front Propagation},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Siqueira, Marcelo and Gee, James C.},
  Journal                  = {IEEE Trans Image Process},
  Year                     = {2011},

  Month                    = jun,
  Number                   = {6},
  Pages                    = {1756--1761},
  Volume                   = {20},

  Doi                      = {10.1109/TIP.2010.2095021},
  ISSN                     = {1057-7149},
  Unique-id                = {ISI:000290732600023}
}

@InProceedings{Tustison2006,
  Title                    = {A generalization of Free-Form Deformation image registration within the ITK finite element framework},
  Author                   = {Tustison, Nicholas J. and Avants, Brian B. and Sundaram, Tessa A. and Duda, Jeffrey T. and Gee, James C.},
  Booktitle                = {BIOMEDICAL IMAGE REGISTRATION, PROCEEDINGS},
  Year                     = {2006},
  Editor                   = {Pluim, JPW and Likar, B and Gerritsen, FA},
  Note                     = {3rd International Workshop on Biomedical Image Registration, Utrecht Univ, Utrecht, NETHERLANDS, JUL 09-11, 2006},
  Organization             = {Philips Med Syst},
  Pages                    = {238--246},
  Series                   = {LECTURE NOTES IN COMPUTER SCIENCE},
  Volume                   = {4057},

  __markedentry            = {[stnava:1]},
  ISBN                     = {3-540-35648-7},
  ISSN                     = {0302-9743},
  Unique-id                = {ISI:000239485200029}
}

@Article{Tustison2014,
  Title                    = {Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements.},
  Author                   = {Tustison, Nicholas J. and Cook, Philip A. and Klein, Arno and Song, Gang and Das, Sandhitsu R. and Duda, Jeffrey T. and Kandel, Benjamin M. and {van Strien}, Niels and Stone, James R. and Gee, James C. and Avants, Brian B.},
  Journal                  = {Neuroimage},
  Year                     = {2014},

  Month                    = oct,
  Pages                    = {166--179},
  Volume                   = {99},

  Abstract                 = {Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan-Killiany-Tourville (DKT) cortical labeling protocol. We found good scan-rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline.},
  Doi                      = {10.1016/j.neuroimage.2014.05.044},
  Institution              = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, USA.},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pii                      = {S1053-8119(14)00409-1},
  Pmid                     = {24879923},
  Timestamp                = {2014.10.27}
}

@Article{Tustison2013a,
  Title                    = {Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences.},
  Author                   = {Tustison, Nicholas J. and Johnson, Hans J. and Rohlfing, Torsten and Klein, Arno and Ghosh, Satrajit S. and Ibanez, Luis and Avants, Brian B.},
  Journal                  = {Front Neurosci},
  Year                     = {2013},
  Pages                    = {162},
  Volume                   = {7},

  Doi                      = {10.3389/fnins.2013.00162},
  Institution              = {Department of Radiology and Medical Imaging, University of Virginia Charlottesville, VA, USA.},
  Language                 = {eng},
  Medline-pst              = {epublish},
  Owner                    = {stnava},
  Pmid                     = {24058331},
  Timestamp                = {2014.08.26}
}

@Article{Tustison2014a,
  Title                    = {Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.},
  Author                   = {Tustison, Nicholas J. and Shrinidhi, K. L. and Wintermark, Max and Durst, Christopher R. and Kandel, Benjamin M. and Gee, James C. and Grossman, Murray C. and Avants, Brian B.},
  Journal                  = {Neuroinformatics},
  Year                     = {2014},

  Month                    = {Nov},

  __markedentry            = {[stnava:]},
  Abstract                 = {Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package-a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" tumor components, respectively.},
  Doi                      = {10.1007/s12021-014-9245-2},
  Institution              = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA, ntustison@virginia.edu.},
  Language                 = {eng},
  Medline-pst              = {aheadofprint},
  Owner                    = {stnava},
  Pmid                     = {25433513},
  Timestamp                = {2015.02.08},
  Url                      = {http://dx.doi.org/10.1007/s12021-014-9245-2}
}

@Article{Wang2010a,
  Title                    = {Estimation of Perfusion and Arterial Transit Time in Myocardium Using Free-Breathing Myocardial Arterial Spin Labeling With Navigator-Echo},
  Author                   = {Wang, Danny J. J. and Bi, Xiaoming and Avants, Brian B. and Meng, Tongbai and Zuehlsdorff, Sven and Detre, John A.},
  Journal                  = {Magn. Reson. Med.},
  Year                     = {2010},

  Month                    = nov,
  Number                   = {5},
  Pages                    = {1289--1295},
  Volume                   = {64},

  Doi                      = {10.1002/mrm.22630},
  ISSN                     = {0740-3194},
  Unique-id                = {ISI:000283616900008}
}

@Article{Wang2010,
  Title                    = {Standing on the shoulders of giants: improving medical image segmentation via bias correction.},
  Author                   = {Wang, Hongzhi and Das, Sandhitsu and Pluta, John and Craige, Caryne and Altinay, Murat and Avants, Brian and Weiner, Michael and Mueller, Susanne and Yushkevich, Paul},
  Journal                  = {Med Image Comput Comput Assist Interv},
  Year                     = {2010},
  Number                   = {Pt 3},
  Pages                    = {105--112},
  Volume                   = {13},

  __markedentry            = {[stnava:1]},
  Abstract                 = {We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.},
  Institution              = {Department of Radiology, University of Pennsylvania, USA.},
  Keywords                 = {Algorithms; Artifacts; Brain, anatomy /\&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {20879389},
  Timestamp                = {2013.05.29}
}

@Article{Wang2011,
  Title                    = {A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation},
  Author                   = {Wang, Hongzhi and Das, Sandhitsu R. and Suh, Jung Wook and Altinay, Murat and Pluta, John and Craige, Caryne and Avants, Brian and Yushkevich, Paul A. and Alzheimers Dis Neuroimaging Initia},
  Journal                  = {Neuroimage},
  Year                     = {2011},

  Month                    = apr,
  Number                   = {3},
  Pages                    = {968--985},
  Volume                   = {55},

  Doi                      = {10.1016/j.neuroimage.2011.01.006},
  ISSN                     = {1053-8119},
  Unique-id                = {ISI:000288313800012}
}

@Article{Weber2013,
  Title                    = {Reproducibility of functional network metrics and network structure: a comparison of task-related BOLD, resting ASL with BOLD contrast, and resting cerebral blood flow.},
  Author                   = {Weber, Matthew J. and Detre, John A. and Thompson-Schill, Sharon L. and Avants, Brian B.},
  Journal                  = {Cogn. Affect. Behav. Neurosci.},
  Year                     = {2013},

  Month                    = sep,
  Number                   = {3},
  Pages                    = {627--640},
  Volume                   = {13},

  Abstract                 = {Network analysis is an emerging approach to functional connectivity in which the brain is construed as a graph and its connectivity and information processing estimated by mathematical characterizations of graphs. There has been little to no work examining the reproducibility of network metrics derived from different types of functional magnetic resonance imaging (fMRI) data (e.g., resting vs. task related, or pulse sequences other than standard blood oxygen level dependent [BOLD] data) or of measures of network structure at levels other than summary statistics. Here, we take up these questions, comparing the reproducibility of graphs derived from resting arterial spin-labeling perfusion fMRI with those derived from BOLD scans collected while the participant was performing a task. We also examine the reproducibility of the anatomical connectivity implied by the graph by investigating test-retest consistency of the graphs' edges. We compare two measures of graph-edge consistency both within versus between subjects and across data types. We find a dissociation in the reproducibility of network metrics, with metrics from resting data most reproducible at lower frequencies and metrics from task-related data most reproducible at higher frequencies; that same dissociation is not recapitulated, however, in network structure, for which the task-related data are most consistent at all frequencies. Implications for the practice of network analysis are discussed.},
  Doi                      = {10.3758/s13415-013-0181-7},
  Institution              = {Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA, mweb@psych.upenn.edu.},
  Keywords                 = {Adult; Brain Mapping, methods; Brain, blood supply/physiology; Cerebrovascular Circulation, physiology; Female; Humans; Magnetic Resonance Imaging, methods; Male; Reproducibility of Results; Rest, physiology; Time Factors; Young Adult},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {23813017},
  Timestamp                = {2014.08.26}
}

@Article{Xie2014,
  Title                    = {Automatic clustering and thickness measurement of anatomical variants of the human perirhinal cortex.},
  Author                   = {Xie, Long and Pluta, John and Wang, Hongzhi and Das, Sandhitsu R. and Mancuso, Lauren and Kliot, Dasha and Avants, Brian B. and Ding, Song-Lin and Wolk, David A. and Yushkevich, Paul A.},
  Journal                  = {Med Image Comput Comput Assist Interv},
  Year                     = {2014},
  Number                   = {Pt 3},
  Pages                    = {81--88},
  Volume                   = {17},

  __markedentry            = {[stnava:1]},
  Abstract                 = {The entorhinal cortex (ERC) and the perirhinal cortex (PRC) are subregions of the medial temporal lobe (MTL) that play important roles in episodic memory representations, as well as serving as a conduit between other neocortical areas and the hippocampus. They are also the sites where neuronal damage first occurs in Alzheimer's disease (AD). The ability to automatically quantify the volume and thickness of the ERC and PRC is desirable because these localized measures can potentially serve as better imaging biomarkers for AD and other neurodegenerative diseases. However, large anatomical variation in the PRC makes it a challenging area for analysis. In order to address this problem, we propose an automatic segmentation, clustering, and thickness measurement approach that explicitly accounts for anatomical variation. The approach is targeted to highly anisotropic (0.4x0.4x2.0mm3 ) T2-weighted MRI scans that are preferred by many authors for detailed imaging of the MTL, but which pose challenges for segmentation and shape analysis. After automatically labeling MTL substructures using multi-atlas segmentation, our method clusters subjects into groups based on the shape of the PRC, constructs unbiased population templates for each group, and uses the smooth surface representations obtained during template construction to extract regional thickness measurements in the space of each subject. The proposed thickness measures are evaluated in the context of discrimination between patients with Mild Cognitive Impairment (MCI) and normal controls (NC).},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {25320785},
  Timestamp                = {2014.11.01}
}

@Article{Yushkevich2010,
  Title                    = {Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: An illustration in ADNI 3~{T MRI} data},
  Author                   = {Yushkevich, Paul A. and Avants, Brian B. and Das, Sandhitsu R. and Pluta, John and Altinay, Murat and Craige, Caryne and Alzheimer's Dis Neuroimaging Initi},
  Journal                  = {Neuroimage},
  Year                     = {2010},

  Month                    = apr,
  Number                   = {2},
  Pages                    = {434--445},
  Volume                   = {50},

  Doi                      = {10.1016/j.neuroimage.2009.12.007},
  ISSN                     = {1053-8119},
  Unique-id                = {ISI:000274948400009}
}

@InProceedings{Yushkevich2006,
  Title                    = {{3D} mouse brain reconstruction from histology using a coarse-to-fine approach},
  Author                   = {Yushkevich, Paul A. and Avants, Brian B. and Ng, Lydia and Hawrylycz, Michael and Burstein, Pablo D. and Zhang, Hui and Gee, James C.},
  Booktitle                = {BIOMEDICAL IMAGE REGISTRATION, PROCEEDINGS},
  Year                     = {2006},
  Editor                   = {Pluim, JPW and Likar, B and Gerritsen, FA},
  Note                     = {3rd International Workshop on Biomedical Image Registration, Utrecht Univ, Utrecht, NETHERLANDS, JUL 09-11, 2006},
  Organization             = {Philips Med Syst},
  Pages                    = {230--237},
  Series                   = {LECTURE NOTES IN COMPUTER SCIENCE},
  Volume                   = {4057},

  __markedentry            = {[stnava:1]},
  ISBN                     = {3-540-35648-7},
  ISSN                     = {0302-9743},
  Unique-id                = {ISI:000239485200028}
}

@Article{Yushkevich2009,
  Title                    = {A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4~{T}},
  Author                   = {Yushkevich, Paul A. and Avants, Brian B. and Pluta, John and Das, Sandhitsu and Minkoff, David and Mechanic-Hamilton, Dawn and Glynn, Simon and Pickup, Stephen and Liu, Weixia and Gee, James C. and Grossman, Murray and Detre, John A.},
  Journal                  = {Neuroimage},
  Year                     = {2009},

  Month                    = jan,
  Number                   = {2},
  Pages                    = {385--398},
  Volume                   = {44},

  Doi                      = {10.1016/j.neuroimage.2008.08.042},
  ISSN                     = {1053-8119},
  Unique-id                = {ISI:000262301100010}
}

@Article{Yushkevich2008,
  Title                    = {Shape-based alignment of hippocampal subfields: evaluation in postmortem {MRI}.},
  Author                   = {Yushkevich, Paul A. and Avants, Brian B. and Pluta, John and Minkoff, David and Detre, John A. and Grossman, Murray and Gee, James C.},
  Journal                  = {Med Image Comput Comput Assist Interv},
  Year                     = {2008},
  Number                   = {Pt 1},
  Pages                    = {510--517},
  Volume                   = {11},

  __markedentry            = {[stnava:1]},
  Abstract                 = {This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. Evaluation takes place in postmortem MRI dataset acquired at 9.4 Tesla with many averages and approximately 0.01 mm3 voxel resolution. Continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. Shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo MRI, which typically lacks features that distinguish hippocampal subfields.},
  Institution              = {Department of Radiology, University of Pennsylvania, USA.},
  Keywords                 = {Algorithms; Artificial Intelligence; Cadaver; Computer Simulation; Hippocampus, anatomy /\&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Information Storage and Retrieval, methods; Magnetic Resonance Imaging, methods; Models, Biological; Models, Statistical; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {18979785},
  Timestamp                = {2013.05.29}
}

@Article{Yushkevich2012,
  Title                    = {From label fusion to correspondence fusion: a new approach to unbiased groupwise registration.},
  Author                   = {Yushkevich, Paul A. and Wang, Hongzhi and Pluta, John and Avants, Brian B.},
  Journal                  = {Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit},
  Year                     = {2012},
  Pages                    = {956--963},

  __markedentry            = {[stnava:1]},
  Abstract                 = {Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate than those based on registration to a population template. In a 2D example in real MRI data, fusion methods result in more consistent mappings between manual segmentations of the hippocampus.},
  Doi                      = {10.1109/CVPR.2012.6247771},
  Institution              = {Department of Radiology, University of Pennsylvania, Philadelphia, USA.},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {24457950},
  Timestamp                = {2014.08.26}
}

@Article{Yushkevich2010a,
  Title                    = {Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted {MRI}},
  Author                   = {Yushkevich, Paul A. and Wang, Hongzhi and Pluta, John and Das, Sandhitsu R. and Craige, Caryne and Avants, Brian B. and Weiner, Michael W. and Mueller, Susanne},
  Journal                  = {Neuroimage},
  Year                     = {2010},

  Month                    = dec,
  Number                   = {4},
  Pages                    = {1208--1224},
  Volume                   = {53},

  Doi                      = {10.1016/j.neuroimage.2010.06.040},
  ISSN                     = {1053-8119},
  Unique-id                = {ISI:000282165800004}
}

@Article{Zhang2007a,
  Title                    = {High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis.},
  Author                   = {Zhang, Hui and Avants, Brian B. and Yushkevich, Paul A. and Woo, John H. and Wang, Sumei and McCluskey, Leo F. and Elman, Lauren B. and Melhem, Elias R. and Gee, James C.},
  Journal                  = {IEEE Trans Med Imaging},
  Year                     = {2007},

  Month                    = nov,
  Number                   = {11},
  Pages                    = {1585--1597},
  Volume                   = {26},

  Abstract                 = {Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. Using an ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three normalization methods representing the current range of available approaches: low-dimensional normalization using the fractional anisotropy (FA), high-dimensional normalization using the FA, and high-dimensional normalization using full tensor information. Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts.},
  Doi                      = {10.1109/TMI.2007.906784},
  Institution              = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA 19104, USA.},
  Keywords                 = {Adult; Aged; Algorithms; Amyotrophic Lateral Sclerosis, pathology; Artificial Intelligence; Brain, pathology; Diffusion Magnetic Resonance Imaging, methods; Female; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Male; Middle Aged; Nerve Fibers, Myelinated, pathology; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng},
  Medline-pst              = {ppublish},
  Owner                    = {stnava},
  Pmid                     = {18041273},
  Timestamp                = {2013.05.29}
}

license_type: permissive

branch_name: refs/heads/master

extension: bib