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An example of a healthy nerve (A) and an atrophied nerve (B) from the multi-atlas segmentation atlas subjects. In the coronal view, ON atrophy is apparent. Quantification of these structural differences is the target of the presented algorithm.

Disambiguating the Optic Nerve from the Surrounding Cerebrospinal Fluid: Application to MS-related Atrophy

Jan. 31, 2016—Robert L. Harrigan, Andrew J. Plassard, Frederick W. Bryan, Gabriela Caires, Louise A. Mawn, Lindsey M. Dethrage, Siddharama Pawate, Robert L. Galloway, Seth A. Smith, Bennett A. Landman. “Disambiguating the Optic Nerve from the Surrounding Cerebrospinal Fluid: Application to MS-related Atrophy.” Magnetic Resonance in Medicine. In press 2014.” Full Text: https://www.ncbi.nlm.nih.gov/pubmed/25754412 Abstract PURPOSE: Our goal...

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:...

Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol

Oct. 31, 2015—Zhoubing Xu, Andrew J. Asman, Rebeccah Baucom, Richard G Abramson, Benjamin K. Poulose, and Bennett A. Landman. “Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol.” PLoS ONE. 2015 Oct 28;10(10):e0141671. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/26509450 Abstract OBJECTIVE: We described and validated a quantitative anatomical labeling protocol for extracting clinically relevant quantitative parameters for...

Efficient Multi-Atlas Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning

Aug. 31, 2015—Zhoubing Xu, Ryan P. Burke, Christopher P. Lee, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Efficient Multi-Atlas Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.” Medical Image Analysis. In press May 2015. † Full Text: http://www.medicalimageanalysisjournal.com/article/S1361-8415(15)00076-6/fulltext Abstract Abdominal segmentation on clinically acquired computed tomography (CT) has been a...

Qualitative results for the optimal multi-atlas segmentation approach for seven subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2-D slice. The bottom rows show pointwise surface distance error of the label fusion estimate for the ONs and the eye globe structure. The proposed multi-atlas pipeline results in reasonably accurate segmentations for the ON structure. However, slight oversegmentations of the ONs can be observed in certain cases (subjects 4 and 7) supporting the results in the volumetry section

Robust Optic Nerve Segmentation on Clinically Acquired CT

Dec. 31, 2014—Robert Harrigan, Swetasudha Panda, Andrew J. Asman, Michael P. DeLisi, Benjamin C. W. Yvernault, Seth A. Smith, Robert L. Galloway, Louise A. Mawn, Bennett A. Landman “Robust Optic Nerve Segmentation on Clinically Acquired CT.” Journal of Medical Imaging. 1(3), 034006 (Dec 17, 2014). † PMC3903299 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/26158064 Abstract The optic nerve (ON) plays a...

Overview of the need for accurate estimates in high performance medical imaging. Complex multi-stage imaging layouts need to provide accurate estimations of their resource requirements in order to leverage high performance computing resources. The upper-right inlay shows histograms of the runtime for a single software module (“File Collection Efficient Registration”) on datasets acquired on 21 individuals

Resource Estimation in High Performance Medical Image Computing

Oct. 31, 2014—Rueben Banalagay, Kelsie J. Covington, D.Mitch Wilkes, Bennett A. Landman. “Resource Estimation in High Performance Medical Image Computing.” Neuroinformatics. 2014 Oct;12(4):563-73. † PMC4381797 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24906466 Abstract Medical imaging analysis processes often involve the concatenation of many steps (e.g., multi-stage scripts) to integrate and realize advancements from image acquisition, image processing, and computational analysis. With the...

Acquired images (left) and manual labels (right) are shown for two controls (a, b), a multiple sclerosis patient (c), and an optic nerve head drusen patient (d). Axial, sagittal, and coronal views of the manually labeled optic nerves, optic chiasm, and eye globes for the subject in (b) are shown in (e).

Evaluation of Multi-Atlas Label Fusion for Orbital Segmentation on In Vivo MRI

Jul. 31, 2014—Swetasudha Panda, Andrew J Asman, Shweta P Khare, Lindsey Thompson, Louise A Mawn, Seth A Smith, Bennett A Landman. “Evaluation of Multi-Atlas Label Fusion for Orbital Segmentation on In Vivo MRI.” Journal of Medical Imaging. 1(2), 024002 (Jul–Sep 2014) † PMC4280790 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/25558466 Abstract Multi-atlas methods have been successful for brain segmentation, but their application...

$Results from the application analysis. Heritability estimates from the twin, nuclear, and grandnuclear families of 80 subjects overlayed on the ENIGMA-DTI fractional anisotropy template and skeleton. The color scale reflects the heritability estimate, with warmer colors suggesting greater heritability. The ground truth heritabilities were derived from the previous literature.$

The impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using SOLAR

Jun. 20, 2014—Mary Ellen Koran, Bo Li, Neda Jahanshad, Tricia A. Thornton-Wells, David C. Glahn, Paul M. Thompson, John Blangero, Thomas E. Nichols, Peter Kochunov, Bennett A. Landman, “The impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using SOLAR”. Journal of Medical Imaging, 2014 Jun 27;1(1):014005 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/25558465 Abstract Imaging...

Simulations with traditional random raters. Coronal sections of the three-dimensional volume show the high resolution MRI image (A), manually drawn truth model (B), an example delineation from one random traditional rater (C), and the results of a STAPLE recombination of three label sets (D). STAPLER enables fusion of label sets when raters provide only partial datasets, but performance suffers with decreasing overlap (E). With training data (F), STAPLER improved the performance even with each rater labeling only a small portion of the dataset. Box plots in E and F show mean, quartiles, range up to 1.5σ, and outliers. The highlighted plot in E indicates the simulation for which STAPLER was equivalent to STAPLE--i.e., all raters provide a complete set of labels.

Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data

Mar. 1, 2010—A. Landman, H. Wan, J. Bogovic, and J. L. Prince. “Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 (Oral Presentation) PMC2917119 Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917119/ Abstract Image labeling and parcellation are critical tasks for the assessment of volumetric and morphometric...

Interpolation of the genu of the Corpus Callosum (CC) and longitudinal fasciculus
(LF) near the lateral ventricle (V): region of interest (ROI) selected on an
orientation color coded anisotropy map (A,B). MD (C) and log-determinant (D) are
shown at the reconstructed resolution. Upsampled (4 × 4) and interpolated contrasts
are shown for linear (E-G), Log-Euclidean (H-J), and CLEAR (K-M) tensor interpolation.
While the linear interpolation has smooth FA and MD (E,F), it exhibits abrupt
changes in the determinant (G). The Log-Euclidean interpolation exhibits FA artifacts
in regions of high anisotropy and curvature (H). CLEAR avoids all these problems

Tensor Interpolation by Concise Local Estimation of Anisotropy and Rotation

Sep. 1, 2008—A. Landman, P-L. Bazin, and J. L. Prince. “Tensor Interpolation by Concise Local Estimation of Anisotropy and Rotation”, In Proceedings of the Workshop on Computational Diffusion MRI at the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, New York, NY, September 2008. Full Text:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.4262&rep=rep1&type=pdf#page=265 Abstract In diffusion tensor imaging, tensors represent the...

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