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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...

Comparison between Traditional DTI and CFARI at SNR of 25:1 and b-value of 1000 s/mm2. (a) Traditional DTI, where fibers having FA<=0.25 are considered to be isotropic component (shown in black). (b) CFARI can resolve multiple crossing fibers.

Effect of Regularization Parameter and Scan Time on Crossing Fibers with Constrained Compressed Sensing

Feb. 1, 2011—Fatma Elzahraa A. ElShahaby, Bennett A. Landman, and Jerry L. Prince. “Effect of Regularization Parameter and Scan Time on Crossing Fibers with Constrained Compressed Sensing”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 PMC3087384 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3087384/ Abstract Diffusion tensor imaging (DTI) is an MR imaging technique that...

With 6 raters hired to identify 82 RV insertion points in 41 randomly selected pig heart slices, the analyzed results of 3 of the slices are shown, where the red “x” are all rater decisions, and the green “o” show the MAP Continuous STAPLE fusion comparing to an expert's decision (yellow “x”). In the first image, the fusion corrects the one rater's mistake that has deviated too much. In the second image, fusion brings the result closer to the expert decision than the scattered rater decisions. In the last image, although rater deviations cause the fusion also to deviate, it is still close to the expert decision.

Statistical Fusion of Continuous Labels: Identification of Cardiac Landmarks

Feb. 1, 2011—Fangxu Xing, Sahar Soleimanifard, Jerry L. Prince, Bennett A. Landman. “Statistical Fusion of Continuous Labels: Identification of Cardiac Landmarks”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 (Oral Presentation) PMC3110005 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3110005/ Abstract Image labeling is an essential task for evaluating and analyzing morphometric features in medical...

The three major tasks presented in this paper and the gamut of their observations. The pig cardiac data can be seen in A), the simulated cylinder data can be seen in B), and the sagittal, axial, and coronal cerebellum data can be seen in C). The range of observations is broken up into three classifications. The good classification represents observations that are high quality observations given the original image slice. The bad classification represents observations where the rules were followed but the labeled images are not necessarily close to the ground truth. The ugly classification represents blatant rule breaking and observations that are completely inconsistent with the expected ground truth.

Foibles, Follies, and Fusion: Assessment of Statistical Label Fusion Techniques for Web-Based Collaborations using Minimal Training

Feb. 1, 2011—Andrew J. Asman, Andrew G. Scoggins, Jerry L. Prince, Bennett A. Landman. “Foibles, Follies, and Fusion: Assessment of Statistical Label Fusion Techniques for Web-Based Collaborations using Minimal Training”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 PMC3083117† Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3083117/ Abstract Labeling or parcellation of structures of interest on...

Representative labeling results. For illustrative purposes, we show the range of observations divided into visually good classification (generally precise), bad classification (rules were followed but the labeled images are not visually close to the truth), and ugly classification (inconsistent with the expected ground truth). All qualities of observations were observed using all input devices.

Characterizing and Optimizing Rater Performance for Internet-based Collaborative Labeling

Feb. 1, 2011—Joshua A. Stein, Andrew J. Asman, Bennett A. Landman. “Characterizing and Optimizing Rater Performance for Internet-based Collaborative Labeling”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 (Oral Presentation) Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3157950/ Abstract Labeling structures on medical images is crucial in determining clinically relevant correlations with morphometric and volumetric features....

JIST graphical layout tool. JIST provides a library of tools (A), a visual scripting area (B), and an auto-generated GUI (C) for direct parameter specification (as opposed to connections from other modules).

Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities

May. 1, 2010—Kelsie Covington, Evan S. McCreedy, Min Chen, Aaron Carass, Nicole Aucoin, and Bennett A. Landman. “Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities.” Biomedical Science and Engineering Conference, Oak Ridge, TN May 2010 (Oral Presentation) PMC2998761 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2998761/ Abstract Clinical research with medical imaging typically involves large-scale data analysis with...