Neuroimaging Category

Segmentation of Malignant Gliomas through Remote Collaboration and Statistical Fusion.

Oct. 31, 2012—Zhoubing Xu, Andrew J. Asman, Eesha Singh, Lola Chambless, Reid Thompson, Bennett A. Landman. “Segmentation of Malignant Gliomas through Remote Collaboration and Statistical Fusion.” Medical Physics. 2012 Oct;39(10):5981-9 PMC3461053 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Segmentation+of+Malignant+Gliomas+through+Remote+Collaboration+and+Statistical+Fusion. Abstract: PURPOSE: Malignant gliomas represent an aggressive class of central nervous system neoplasms. Correlation of interventional outcomes with tumor morphometry data necessitates 3D...

Biological Parametric Mapping Accounting for Random Regressors with Regression Calibration and Model II Regression

Sep. 1, 2012—Xue Yang, Carolyn B. Lauzon, Ciprian Crainiceanu, Brian Caffo, Susan M. Resnick, Bennett A. Landman. “Biological Parametric Mapping Accounting for Random Regressors with Regression Calibration and Model II Regression.” NeuroImage. 2012 Sep;62(3):1761-8. PMC22609453 Full text: https://www.ncbi.nlm.nih.gov/pubmed/22609453 Abstract Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional...

Registered atlases exhibit spatially varying behavior. Representative slices from an expertly labeled MR brain image and CT head and neck image are shown in (A). Example registered atlases with their local performance can be seen in (B) and (C).

Formulating Spatially Varying Performance in the Statistical Fusion Framework

Jul. 31, 2012—Andrew J. Asman and Bennett A. Landman, “Formulating Spatially Varying Performance in the Statistical Fusion Framework”, IEEE Transactions on Medical Imaging. 2012 Jun;31(6):1326-36. PMC3368083 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3368083/ Abstract To date, label fusion methods have primarily relied either on global (e.g. STAPLE, globally weighted vote) or voxelwise (e.g. locally weighted vote) performance models. Optimality of...

The gamut of observed slices. The labeling objective was to a) delineate the gadolinium enhancing tumor core (T1 images, 1st row) and b) label all abnormally bright tissue (T2 images, 2nd row). The experienced labeler (“truth”, 2nd column) accurately found all structures, while the millers' work spanned a wide range of accuracies.

Collaborative Labeling of Malignant Glioma

May. 4, 2012—Zhoubing Xu, Andrew J. Asman, Eesha Singh, Lola Chambless, Reid Thompson, and Bennett A. Landman, “Collaborative Labeling of Malignant Glioma”, In Proceedings of the 2012 International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain Full text: http://ieeexplore.ieee.org/abstract/document/6235763/ Abstract Malignant gliomas represent an aggressive class of central nervous system neoplasms which are often treated by maximal surgical resection....

Empirical experiment using axial cross-section of cerebellar data to assess the performance of STAPLE (on a slice-by-slice basis) and STAPLER (volumetric fusion). The representative slices shown in (A) – (C) present an example truth model, and observations by minimally trained undergraduate students, respectively. The slices seen in (D) and (E) are the estimated labels by STAPLE and STAPLER, respectively. The plot on the top of (F) shows the accuracy on a per slice basis of the observations (box plots) STAPLER (green), STAPLE (blue), and majority vote (red). The histogram on the bottom of (F) shows the number of observations per slice. Lastly, the plot seen in (G) shows the difference in DSC between STAPLER, STAPLE, and majority vote on a per label basis. The legend for these label numbers can be seen at the bottom of (G).

Robust Statistical Fusion of Image Labels

Feb. 1, 2012—Bennett A. Landman, Andrew J. Asman, Drew Scoggins, John A. Bogovic, Fangxu Xing, and Jerry L. Prince. “Robust Statistical Fusion of Image Labels”, IEEE Transactions on Medical Imaging. 2012 Feb;31(2):512-22. PMC3262958 Full text: https://www.ncbi.nlm.nih.gov/pubmed/22010145 Abstract Image labeling and parcellation (i.e., assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric...

Comparison of DTI and HARDI direction estimates. Mean unweighted scans (A, E) and DTI colormap images (B, F) are shown for two representative slices. Enlargements show estimated diffusion tensors (C, G) alongside CFARI directions (D, H) based on two repetitions of 30 directions at a b-value of 700 s/mm2

Resolution of Crossing Fibers with Constrained Compressed Sensing using Diffusion Tensor MRI

Feb. 1, 2012—Bennett A. Landman, John A Bogovic; Hanlin Wan; Fatma El Zahraa ElShahaby; Pierre-Louis Bazin, and Jerry L Prince. “Resolution of Crossing Fibers with Constrained Compressed Sensing using Diffusion Tensor MRI”, NeuroImage. 2012 Feb 1;59(3):2175-86. PMC22019877 Full text: https://www.ncbi.nlm.nih.gov/pubmed/22019877 Abstract Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. In regions...

Finding Seeds for Segmentation Using Statistical Fusion

Feb. 1, 2012—Fangxu Xing, Andrew J. Asman, Jerry L. Prince, Bennett A. Landman. “Finding Seeds for Segmentation Using Statistical Fusion.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 Full text: https://www.ncbi.nlm.nih.gov/pubmed/23019385 Abstract Image labeling is an essential step for quantitative analysis of medical images. Many image labeling algorithms require seed identification in order...

Towards Automatic Quantitative Quality Control for MRI

Feb. 1, 2012—C. Lauzon, B. Caffo, and B. Landman. “Towards Automatic Quantitative Quality Control for MRI.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) Full text: https://www.ncbi.nlm.nih.gov/pubmed/23087586 Abstract Quality and consistency of clinical and research data collected from Magnetic Resonance Imaging (MRI) scanners may become suspect due to a wide variety...

Figure 2
Illustration of minimally rater performance relative to an experienced rater for T1 data (A) and T2 data (B). The top row shows 200 slices which have been expertly labeled sorted by the median overlap with minimally trained raters. The center row shows the number of times each slice has been labeled. The final row illustrates an arbitrary example from the lowest, median, and highest accuracy slices.

Collaborative Labeling of Malignant Glioma with WebMILL: A First Look

Feb. 1, 2012—E. Singh, Z. Xu, A. Asman, L. Chambless, R. Thompson and B. Landman. “Collaborative Labeling of Malignant Glioma with WebMILL: A First Look.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Collaborative+Labeling+of+Malignant+Glioma+with+WebMILL%3A+A+First+Look Abstract Malignant gliomas are the most common form of primary neoplasm in the central nervous...

Figure 3
Kurtosis of weighted residuals. The Kurtosis maps of the residuals from the weighted data are shown in the first column. The middle and the right column display one voxel residuals across the first 100 scans inside the cerebrospinal fluid region and the white matter region respectively.

A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T

Feb. 1, 2012—Xue Yang, Martha J. Holmes, Allen T. Newton, Victoria L. Morgan, Bennett A. Landman. “A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) NIHMS341654† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=A+Comparison+of+Distributional+Considerations+with+Statistical+Analysis+of+Resting+State+fMRI+at+3T+and+7T Abstract Ultra-high field 7T magnetic resonance...

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