Medical-image Analysis and Statistical Interpretation (MASI) Lab

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

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...

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...

Volumetric qualitative comparison of the accuracy of the segmented gray matter for the pairwise slice-based framework, and the proposed groupwise slice-based framework. The proposed registration framework consistently estimates the complex shape of the GM horn more accurately than its pairwise counterpart. Note the different axes for the two different registration frameworks.

Groupwise Multi-Atlas Segmentation of the Spinal Cord’s Internal Structure.

Apr. 15, 2014—Andrew J. Asman, Frederick W. Bryan, Seth A. Smith, Daniel S. Reich, Bennett A. Landman. “Groupwise Multi-Atlas Segmentation of the Spinal Cord’s Internal Structure.” Medical Image Analysis (MedIA). 2014 Feb 5;18(3):460-471. PMC24556080† Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009677/ Abstract The spinal cord is an essential and vulnerable component of the central nervous system. Differentiating and localizing the spinal...

Qualitative fusion results. A representative slice shows the effect of the additional confidence parameter on the output of both the voting and statistical fusion techniques. The three rater observations (one submitted empty) are shown. In this example, CWV and “Best Pick” would result in an identical segmentation.

Self-Assessed Performance Improves Statistical Fusion of Image Labels.

Feb. 15, 2014—Frederick W. Bryan, Zhoubing Xu, Andrew J. Asman, Wade M. Allen, Daniel S. Reich, and Bennett A. Landman. “Self-Assessed Performance Improves Statistical Fusion of Image Labels.” Medical Physics. 2014 Mar;41(3):031903. PMC24593721† Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3978333/ Abstract Purpose: Expert manual labeling is the gold standard for image segmentation, but this process is difficult, time-consuming, and prone...

Figure 3
Qualitative results for the optimal multi-atlas segmentation approach for 7 subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2D slice. The bottom rows show point-wise 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 over segmentations of the ONs can be observed in certain cases (subjects 4 and 7).

Robust Optic Nerve Segmentation on Clinically Acquired CT

Feb. 1, 2014—Swetasudha Panda, Andrew J. Asman, Michael P. DeLis, Louise A. Mawn, Robert L. Galloway, Bennett A. Landman. “Robust Optic Nerve Segmentation on Clinically Acquired CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817810 Abstract The optic nerve is a sensitive central nervous system structure,...

Figure 4
Rendering of the manually labeled cortical regions ().

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

Feb. 1, 2014—Yurui Gao, Shweta P. Khare, Swetasudha Panda, Ann S Choe, Iwona Stepniewska, Xia Li, Zhoahua Ding, Adam Anderson, Bennett A. Landman, “A brain MRI atlas of the common squirrel monkey, Saimiri sciureus.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817811 Abstract The common squirrel...

Figure 3
Flowchart of the proposed method. The atlas labels are co-registered to construct a pose-free implicit parametric shape model, including the mean and the modes of variation of the spleen shape. The atlas images are registered to the target image, based on which the atlas labels are propagated to the target space. The locally weighted vote yields the initial fusion result of the registered atlas labels weighted by the local intensity similarity between the registered atlas images and the target image in the form of a fuzzy estimate of label probability and a binary estimate of spleen segmentation. The binary image of the mean shape from the pre-constructed spleen shape model is registered to that of the current estimate with two distinct effective ranges, i.e., (1) the whole volume of both image and (2) the mean shape region, of the similarity metric of registration so that the pose-free shape model is transformed into the target space. The current estimate of the spleen is then projected to the two registered shape models. The shape projections are converted into probabilistic priors to adjust the label probability from locally weighted vote, and then generate a new estimate of the spleen. The estimate can be refined with iterative adjustment.

Shape-Constrained Multi-Atlas Segmentation of Spleen in CT

Feb. 1, 2014—Z. Xu, B. Li, S. Panda, A. Asman, Kristen. L. Merkle, Peter L. Shanahan, Richard G. Abramson, B. Landman. “Shape-Constrained Multi-Atlas Segmentation of Spleen in CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Shape-Constrained+Multi-Atlas+Segmentation+of+Spleen+in+CT Abstract Spleen segmentation on clinically acquired CT data is a challenging problem given...

On study design in neuroimaging heritability analyses.

Feb. 1, 2014—Mary Ellen Koran, Neda Jahanshad, Bo Li, Tricia A. Thornton-Wells, David C. Glahn, Paul M. Thompson, John Blangero, Thomas E. Nichols, Peter Kochunov, Bennett A. Landman. “On study design in neuroimaging heritability analyses.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014† Full Text: https://www.researchgate.net/publication/269322665_On_Study_Design_in_Neuroimaging_Heritability_Analyses Abstract Imaging genetics is an emerging...

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...

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