Medical-image Analysis and Statistical Interpretation (MASI) Lab

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:...

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...

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...

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...

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

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...

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...

Twelve organs of interest (left) and registration examples of variable qualities for one target image (right).

Efficient Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning

Feb. 12, 2015—Zhoubing Xu, Ryan P. Burke, Christopher P. Lee, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Efficient Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405802/ Abstract Abdominal segmentation on clinically acquired computed tomography...

The proposed general pipeline for conducting registrations and followed by metric analyses. Initially, each of 20 CT scans were pairwise linear registered using affine IRTK. Using the linear registration as a baseline, the registrations went through four non-rigid registrations. The output non-rigid registrations are evaluated against the manual segmentation via the DSC overlap, mean surface distance, and Hausdorff distance for each organ of interest. The comparison of the four non-rigid registrations was first based on all pairs of inter-subject registrations for all organs. Then another round of comparison was applied to the top 1 and top 5 registrations selected retrospectively for each target CT scan.

Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.

Feb. 12, 2015—Christopher P. Lee, Zhoubing Xu, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405654/ Abstract Image registration has...

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