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$(A) Significant associations with plasma C22:0 and subsequent FA. (A) Red–yellow indicates higher plasma C22:0 associated with lower FA in white matter regions, while cyan–blue indicates higher plasma C22:0 associated with higher FA. Regional statistics are overlaid onto an FA image from a single subject; results are not corrected for multiple comparisons. (B) ACR FA by mean centered Cer 22:0. (B) Association between FA in ACR and Cer 22:0. The residual FA of ACR was calculated by regressing standardized FA on baseline age, sex, interval between blood draw and DTI, diabetes, BMI, scanner, and ratio of white matter lesions to intracranial volume. This is shown plotted against mean centered Cer 22:0 with a linear fit overlaid. Abbreviations: ACR, anterior corona radiata; ALIC, anterior limb of internal capsule; BMI, body mass index; CG, cingulum of cingulate gyrus; DTI, diffusion tensor imaging; FA, fractional anisotropy; Scc, splenium of corpus callosum; SLF, superior longitudinal fasciculus; SS, sagittal stratum. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)$

Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.

Jul. 31, 2016—Christopher E. Gonzalez, Vijay K. Venkatraman, Yang An, Bennett A. Landman, Christos Davatzikos, Veera Venkata Ratnam Bandaru, Norman J. Haughey, Luigi Ferruci, Michelle M. Mielke, Susan M. Resnick. “Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.” Neurobiology of Aging. July 2016. Volume 43, Pages 156–163 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/27255825 Abstract Sphingolipids...

(A) A standard fully connected neural network where each layer's node is connected to each node from the
previous layer.7 (B) A convolutional neural network connecting a covolutional layer to a pooling layer.

Deep Learning for Brain Tumor Classification

Jul. 1, 2016—Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri. “Deep Learning for Brain Tumor Classification.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is...

Quality analysis reports generally perform first level analyses, including diffusion tensor imaging (A) (Asman and Landman, 2013; Lauzon et al., 2013b; Lauzon and Landman, 2013; Papadakis et al., 2003; Whitcher et al., 2008), func-
tional MRI (B) (Friedman and Glover, 2006), multi-atlas brain segmentation (C) (Asman and Landman, 2011, 2013; Ourselin et al., 2001, 2002), white matter labeling (D) (Plassard et al., 2015), registration to template spaces (E) (Fonov et al.,2009), FreeSurfer (F) (Fischl, 2012) and TRACULA (G) (Yendiki et al., 2011).

Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment

Jan. 31, 2016—Robert L. Harrigan, Benjamin C. Yvernault, Brian D. Boyd, Stephen M. Damon, Kyla David Gibney, Benjamin N. Conrad, Nicholas S. Phillips, Baxter P. Rogers, Yurui Gao, Bennett A. Landman “Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment” Neuroimage, 2014. In press May 2015† Full Text:...

Integration of the Java Image Science Toolkit with E-Science Platform

Jan. 31, 2016—S. Damon, S. Panjwani, S. Bao, P. Kochunov, B. Landman, Integration of the Java Image Science Toolkit with E-Science Platform. 2016. InSight Journal. #963 Full text: http://insight-journal.org/browse/publication/963 Abstract Medical image analyses rely on diverse software packages assembled into a “pipeline”. The Java Image Science Toolkit (JIST) has served as a standalone plugin into the Medical...

DICOM can be converted into research file formats viewed with SPM[7], MIPAV[8]. 3D Slicer[9]. MRIcron[10], MATLAB (Mathworks, Natick, MA)

Validating DICOM transcoding with an open multi-format resource

Oct. 31, 2014—Benjamin C. Yvernault, Charles D. Theobald, Jr., Jolinda C. Smith, Victoria Villalta, David H. Zald, Bennett A. Landman. “Validating DICOM transcoding with an open multi-format resource.” Neuroinformatics. 2014 Oct;12(4):615-7. † PMC4391369 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24777387 Abstract The Digital Imaging and Communications in Medicine (DICOM) standard has allowed wide-scale interoperability between medical imaging devices allowed for construction...

Applying the Algorithm ‘Assessing Quality Using Image Registration Circuits’ (AQUIRC) to Multi-Atlas Segmentation.

Feb. 1, 2014—Datteri, R.D., Asman, A.J., Landman, B.A., Dawant, B.M., “Applying the Algorithm ‘Assessing Quality Using Image Registration Circuits’ (AQUIRC) to Multi-Atlas Segmentation.” Proc. SPIE, Medical Imaging 2014: Image Processing, February 2014. † Full Text: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1852464 Abstract Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a...

Resilience features capture the stability of an inference method to data decimation. An rs-fMRI dataset (1) is temporally decimated into subsets; each inference method (2) is applied independently to each subset; voxel-wise statistics (3) are estimated; and the parameter maps (4) capture spatial dependencies.

Quantitative Evaluation of Statistical Inference in Resting State Functional MRI

Sep. 30, 2012—Xue Yang and Bennett A. Landman. “Quantitative Evaluation of Statistical Inference in Resting State Functional MRI”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, September 2012 (32% acceptance rate) Full text: https://www.ncbi.nlm.nih.gov/pubmed/23286055 Abstract Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional...

Registered atlases exhibit spatially varying behavior. Representative slices from an expertly labeled MR brain image and CT head and neck image are shown in (A). Example registered atlases with their local performance can be seen in (B) and (C).

Formulating Spatially Varying Performance in the Statistical Fusion Framework

Jul. 31, 2012—Andrew J. Asman and Bennett A. Landman, “Formulating Spatially Varying Performance in the Statistical Fusion Framework”, IEEE Transactions on Medical Imaging. 2012 Jun;31(6):1326-36. PMC3368083 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3368083/ Abstract To date, label fusion methods have primarily relied either on global (e.g. STAPLE, globally weighted vote) or voxelwise (e.g. locally weighted vote) performance models. Optimality of...

A Surgeon Specific Automatic Path Planning Algorithm for Deep Brain Stimulation

Feb. 1, 2012—Yuan Liu, Benoit M. Dawant, Srivatsan Pallavaram, Joseph S. Neimat, Pierre-François D’Haese, Ryan D. Datteri, Bennett A. Landman and Jack H. Noble. “A Surgeon Specific Automatic Path Planning Algorithm for Deep Brain Stimulation.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) † Full Text: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1285627 Abstract In deep brain...

Comparison of existing and proposed labeling approaches. In a traditional context, either an expert rater with extensive anatomical knowledge evaluates each dataset (A) or a small set of well-training domain experts who have been instructed by an anatomical expert (B) label each image. Intra- and inter-rater reproducibility analyses are typically performed a per-protocol basis rather than on all datasets. In the proposed WebMILL approach (C), a computer system divides the set of images to be labeled into simple puzzles consisting of a piece of a larger volume and distributes these challenges to a distributed collection of minimally training individuals. Each piece is labeled multiple times by multiple raters. A statistical fusion process simultaneously estimates the true label for each pixel and performance characteristics of each rater.

Foibles, Follies, and Fusion: Web-Based Collaboration for Medical Image Labeling

Jan. 31, 2012—Bennett A Landman, Andrew J Asman, Andrew G Scoggins, John A Bogovic, Joshua A Stein; Jerry L Prince, “Foibles, Follies, and Fusion: Web-Based Collaboration for Medical Image Labeling”, NeuroImage. 2012 Jan 2;59(1):530-9. PMC3195954 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3195954/ Abstract Labels that identify specific anatomical and functional structures within medical images are essential to the characterization of...

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