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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...

Example renderings of (A) BrainCOLOR cortical and sub-cortical labels, (B) Eve cortical and white matter labels and (C) rectified BrainCOLOR and Eve cortical and white matter labels.

Evaluation of Atlas-Based White Matter Segmentation with Eve

Feb. 12, 2015—Andrew J. Plassard, Kendra E. Hinton, Christopher Gonzalez, Vijay Venkatraman, Susan M. Resnick, Bennett A. Landman. “Evaluation of Atlas-Based White Matter Segmentation with Eve” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405655/ Abstract Multi-atlas labeling has come in wide spread use for whole brain labeling on magnetic...

Intensity profiles and spatial distributions of 12 organs of interest (a–l) and a rendering of a manual segmentation (m). Note that the plotted ranges of the 12 intensity profiles are consistent, i.e., −200 ~ 400 Hounsfield unit (HU).

Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models

Feb. 12, 2015—Ryan P. Burke, Zhoubing Xu, Christopher P. Lee, Rebeccah Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405670/ Abstract Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing...

Comparison of the ON of a healthy control (A) and an MS patient with severe ON atrophy (B). Red lines in the axial images show the location of the coronal slices to the right.

“Constructing a statistical atlas of the radii of the optic nerve and cerebrospinal fluid sheath in young healthy adults.

Feb. 12, 2015—Robert L Harrigan, Andrew J. Plassard, Louise A. Mawn, Robert L. Galloway, Seth A. Smith, Bennett A. Landman. “Constructing a statistical atlas of the radii of the optic nerve and cerebrospinal fluid sheath in young healthy adults.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405797/ Abstract Optic...

Flowchart of the ImageNet approach applied the the anonylized imaging data from clinical radiology.

Toward content based image retrieval with deep convolutional neural networks

Feb. 12, 2015—Judah E. Sklan, Andrew J. Plassard, Daniel Fabbri, Bennett A. Landman. “Toward content based image retrieval with deep convolutional neural networks ” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=%E2%80%9CToward+content+based+image+retrieval+with+deep+convolutional+neural+networks+%E2%80%9D Abstract Content–based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and...

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/...

Improving Conceptual Understanding of Signals and Systems in Undergraduate Engineering Students Using Collaborative In-Class Laboratory Exercises

Jun. 12, 2014—Rao, A. S., Fan, J., Brame, C. J., Landman, B. A., “Improving Conceptual Understanding of Signals and Systems in Undergraduate Engineering Students Using Collaborative In-Class Laboratory Exercises.” In American Society for Engineering Education Annual Conference & Exposition. June 2014† Full text: https://www.asee.org/public/conferences/32/papers/9790/view

Representative one voxel t-values as data are randomly decimated. An rs-fMRI dataset is randomly diminished into N data size levels with M subsets in each level. Inference methods are applied to each subset to estimate voxel-wise t-maps. The highlighted point (left) indicates the rs-fMRI seed region.

Evaluation of Statistical Inference on Empirical Resting State fMRI.

May. 31, 2014—Xue Yang, Hakmook Kang, Allen T. Newton, Bennett A. Landman, “Evaluation of Statistical Inference on Empirical Resting State fMRI.” IEEE Transactions on Biomedical Engineering. IEEE Trans Biomed Eng. 2014 Apr;61(4):1091-9. PMC24658234† Full text: https://www.ncbi.nlm.nih.gov/pubmed/24658234 Abstract Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional magnetic resonance imaging...

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