March, 2010

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...

Simulations with traditional random raters. Coronal sections of the three-dimensional volume show the high resolution MRI image (A), manually drawn truth model (B), an example delineation from one random traditional rater (C), and the results of a STAPLE recombination of three label sets (D). STAPLER enables fusion of label sets when raters provide only partial datasets, but performance suffers with decreasing overlap (E). With training data (F), STAPLER improved the performance even with each rater labeling only a small portion of the dataset. Box plots in E and F show mean, quartiles, range up to 1.5σ, and outliers. The highlighted plot in E indicates the simulation for which STAPLER was equivalent to STAPLE--i.e., all raters provide a complete set of labels.

Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data

Mar. 1, 2010—A. Landman, H. Wan, J. Bogovic, and J. L. Prince. “Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 (Oral Presentation) PMC2917119 Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917119/ Abstract Image labeling and parcellation are critical tasks for the assessment of volumetric and morphometric...