July, 2011

Increased sensitivity to outliers with BPM. Considered a simple one regressor situation, model y = βx + μ is used to fit data points in voxel wise analysis. In SPM, the imaging data (left column) lies along the y axis. (i.e., imaging data is treated as y in the fitted model.) The left column shows two sets of images data, PET blood flow maps (top row) and gray matter (GM) tissue density maps (lower row). The middle column shows the design matrix for each regression. Considered one voxel within mis-registration region, in separated analyses of these datasets with SPM (top and lower rows), a mis-registration induced outlier impacts only on data point in the y-direction. In a joint analysis with BPM, the outlier impacts both the x-axis and y-axis, and can result in dramatically different model fits. In the simulation the true β is a small negative value, but the outliers increase the estimated value of β to make it approximately zero or even a large positive number.

Biological Parametric Mapping WITH Robust AND Non-Parametric Statistics

Jul. 31, 2011—Xue Yang, Lori Beason-Held, Susan M Resnick, Bennett A Landman. “Biological Parametric Mapping with Robust and Non-Parametric Statistics”, NeuroImage 57 (2011) Jul 423–430 PMC3114289† Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114289/ Abstract Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image...

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...