Neuroimaging Category

Figure 2
The pipeline for iterative definition of one bundle – the left cingulum in native space of one monkey.

A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging.

Feb. 27, 2016—Yurui Gao, Prasanna Parvathaneni, Kurt G. Schilling, Zhoubing Xu, Ann S. Choe, Bennett A. Landman, Adam M. Anderson. A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text Abstract...

Improving Cerebellar Segmentation with Statistical Fusion

Feb. 27, 2016—Andrew J. Plassard, Zhen Yang, Swati D. Rane, Jerry L. Prince, Daniel O. Claassen, Bennett A. Landman. “Improving Cerebellar Segmentation with Statistical Fusion. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Improving+Cerebellar+Segmentation+with+Statistical+Fusion Abstract The cerebellum is a somatotopically organized central component of the central nervous system well known...

Intermediary results from image synthesis. The cited values for T2-relaxation in gray matter, white matter are approximately 100 and 80 ms respectively. The cited values for T1-relaxation in the gray matter and white matter are approximately 1350 and 800 ms respectively. These values appear consistent with the intermediary results of image synthesis.

One the Fallacy of Quantitative Segmentation for T1-Weighted MRI.

Feb. 15, 2016—Andrew J. Plassard, Robert L. Harrigan, Allen T. Newton, Swati D. Rane, Srivatsan Pallavaram, Pierre F. D’Haese, Benoit M. Dawant, Daniel O. Claassen, Bennett A. Landman. “One the Fallacy of Quantitative Segmentation for T1-Weighted MRI.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845960/ Abstract T1-weighted...

Results for prediction of high-risk discharge GCS cases (a) Average ROC curves internal cross-validated sets (b) Average effect of each new feature that is added via the greedy feature selection method (c) ROC curve for the holdout dataset (d) Effect of each new feature that is added via the greedy feature selection method in the holdout dataset.

A Bayesian Framework for Early Risk Prediction in Traumatic Brain Injury

Feb. 15, 2016—Shikha Chaganti, Andrew J. Plassard, Laura Wilson, Miya A. Smith, Mayur B. Patel, Bennett A. Landman. A Bayesian Framework for Early Risk Prediction in Traumatic Brain Injury. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845965/ Abstract Early detection of risk is critical in determining the course...

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:...

Multi-atlas Learner Fusion: An efficient segmentation approach for large-scale data

Dec. 26, 2015—Andrew J. Asman, Yuankai Huo, Andrew J. Plassard, and Bennett A. Landman, “Multi-atlas Learner Fusion: An efficient segmentation approach for large-scale data”, Medical Image Analysis (MedIA), 2015 Dec;26(1):82-91. Full text: http://linkinghub.elsevier.com/retrieve/pii/S1361-8415(15)00135-8 Abstract We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on...

Fig-1-Flowchart-of-training-a-data-driven-dictionary-of-whole-brain-probabilistic

Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation

Oct. 4, 2015—Yuankai Huo, Katherine Swett, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman. “Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation”, MICCAI MAPPING Workshop, Munich, Germany, October 2015. Full text: https://www.researchgate.net/publication/303483865_Data-driven_Probabilistic_Atlases_Capture_Whole-brain_Individual_Variation Abstract

Flowchart demonstrating the proposed data mapping design for integration of Histology with MRI using block face photography to establish an intermediate common space in coordinates without loss of resolution while addressing space constraint.

Integrating histology and MRI in the first digital brain atlas of the common squirrel monkey, Saimiri sciureus

Feb. 12, 2015—Peizhen Sun, Prasanna Parvathaneni, Yurui Gao, Kurt G. Schilling, Vaibhav A. Janve, Adam W. Anderson, Bennett A. Landman. “Integrating histology and MRI in the first digital brain atlas of the common squirrel monkey, Saimiri sciureus.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405811/ Abstract This effort is...

Revealing Latent Value of Clinically Acquired CTs of Traumatic Brain Injury Through Multi-Atlas Segmentation in a Retrospective Study of 1,003 with External Cross-Validation

Feb. 1, 2015—Andrew J. Plassard, Patrick D. Kelly, Andrew J. Asman, Hakmook Kang, Mayur B. Patel, Bennett A. Landman. “Revealing Latent Value of Clinically Acquired CTs of Traumatic Brain Injury Through Multi-Atlas Segmentation in a Retrospective Study of 1,003 with External Cross-Validation” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405676/...

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...

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