Quantitative Evaluation of Statistical Inference in Resting State Functional MRI
Sep. 30, 2012—Xue Yang and Bennett A. Landman. “Quantitative Evaluation of Statistical Inference in Resting State Functional MRI”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, September 2012 (32% acceptance rate) Full text: https://www.ncbi.nlm.nih.gov/pubmed/23286055 Abstract Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional...
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...
Next Generation of the JAVA Image Science Toolkit (JIST) Visualization and Validation
Aug. 1, 2012—Bo Li, Frederick Bryan, Bennett A. Landman, “Next Generation of the JAVA Image Science Toolkit (JIST) Visualization and Validation.” Insight Journal. August 2012. P 874 PMC4181667 Full text: https://www.ncbi.nlm.nih.gov/pubmed/25285310 Abstract Modern medical imaging analyses often involve the concatenation of multiple steps, and neuroimaging analysis is no exception. The Java Image Science Toolkit (JIST) has provided a...
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...
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....
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...
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...
Automating PACS Quality Control with the Vanderbilt Image Processing Enterprise Resource
Feb. 1, 2012—M. L. Esparza, E. B. Welch and B. A. Landman. “Automating PACS Quality Control with the Vanderbilt Image Processing Enterprise Resource.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) Full text: https://www.ncbi.nlm.nih.gov/pubmed/24357910 Abstract Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic...