Medical-image Analysis and Statistical Interpretation (MASI) Lab

The spatial quality variation exhibited by registered atlases. A representative slice from the true labels (manually drawn) of a target brain is presented in (A). Four example observations of this slice can be seen in (B). The quality of the observations seen in (B) is compared to the true labels seen in (A) to construct the spatial quality variation heat maps presented in (C). Note that the spatial variation is independent of the actual labels of the brain.

Characterizing Spatially Varying Performance to Improve Multi-Atlas Multi-Label Segmentation

Jul. 30, 2011—Andrew J. Asman and Bennett A. Landman. “Characterizing Spatially Varying Performance to Improve Multi-Atlas Multi-Label Segmentation”, In Proceedings of the 2011 International Conference on Information Processing in Medical Imaging (IPMI), Irsee, Bavaria, July 2011 (Oral presentation) (28% acceptance rate) PMC3140117 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3140117/ Abstract Segmentation of medical images has become critical to building understanding of biological...

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...

mpact of outliers in BPM. (A) is one of the regressor images without outliers. The main modality images are: Y = 3X inside a spherical region, Y = constant outside the region. (B) is the Beta estimation when there are no outliers. (C) is the significant results when there are no outliers, (D) is the shifted regressor image, the center cube shifts to another region. The center cube of the paired main modality image shifts to the same region. (E) is the estimated Beta from original BPM when there are outliers. (F) is the significant results from original BPM when there are outliers.

“Robust Biological Parametric Mapping: An Improved Technique for Multimodal Brain Image Analysis”

Feb. 1, 2011—Xue Yang, Lori Beason-Held, Susan M. Resnick, Bennett A. Landman. “Robust Biological Parametric Mapping: An Improved Technique for Multimodal Brain Image Analysis”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 PMC3103184 † Full text Abstract Mapping the quantitative relationship between structure and function in the human brain is an...

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....

A single registered slice from the baseline images in the multi-parametric study of a representative subject. Structural scans (a–c) reveal high resolution information on local tissue classification. Diffusion (d–e), quantitative T2 (f–g), magnetization transfer (h–i), and quantitative T1 (j–k) provide assessments of local micro-structure. Field maps (l–n) enable improved calibration of quantitative findings. Assessment of perfusion (o–p) allows inference on functional characteristics.

Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study

Jan. 31, 2011—Bennett A. Landman, Alan J. Huang, Aliya Gifford, Deepti S. Vikram, Issel Anne L. Lim, Jonathan A.D. Farrell, John A. Bogovic, Jun Hua, Min Chen, Samson Jarso, Seth A. Smith, Suresh Joel, Susumu Mori, James J. Pekar, Peter B. Barker, Jerry L. Prince, and Peter C.M. van Zijl. “Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study”,...

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...

MIPAV (A) automatically detects the presence of the Plug-In Selector and provides a menu item to open the tool. The Plug-In Selector interface lists all detected plug-ins according to their programmer specified hierarchy (B). Once selected from this menu, a programmatically generated GUI appears for any tool (C) which can operate on any image accessible to MIPAV.

The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software

Mar. 31, 2010—Blake C. Lucas, John A. Bogovic, Aaron Carass, Pierre-Loius Bazin, Jerry L. Prince, and Bennett A. Landman, “The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software”, Neuroinformatics. 8(1):5-17.(2010) PMC2860951 Full text: https://www.ncbi.nlm.nih.gov/pubmed/20077162 Abstract Non-invasive neuroimaging techniques enable extraordinarily sensitive and specific in vivo study of the structure, functional response and...

Statistical Fusion of Surface Labels provided by Multiple Raters

Mar. 1, 2010—Bogovic, B. A. Landman, P.-L. Bazin, and J. L. Prince. “Statistical Fusion of Surface Labels provided by Multiple Raters, Over-complete, and Ancillary Data”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 PMC2997739 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Statistical+Fusion+of+Surface+Labels+provided+by+Multiple+Raters Abstract Studies of the size and morphology of anatomical structures rely on accurate and reproducible delineation...

« Previous Page