Author

Comparison of existing and proposed labeling approaches. In a traditional context, either an expert rater with extensive anatomical knowledge evaluates each dataset (A) or a small set of well-training domain experts who have been instructed by an anatomical expert (B) label each image. Intra- and inter-rater reproducibility analyses are typically performed a per-protocol basis rather than on all datasets. In the proposed WebMILL approach (C), a computer system divides the set of images to be labeled into simple puzzles consisting of a piece of a larger volume and distributes these challenges to a distributed collection of minimally training individuals. Each piece is labeled multiple times by multiple raters. A statistical fusion process simultaneously estimates the true label for each pixel and performance characteristics of each rater.

Foibles, Follies, and Fusion: Web-Based Collaboration for Medical Image Labeling

Jan. 31, 2012—Bennett A Landman, Andrew J Asman, Andrew G Scoggins, John A Bogovic, Joshua A Stein; Jerry L Prince, “Foibles, Follies, and Fusion: Web-Based Collaboration for Medical Image Labeling”, NeuroImage. 2012 Jan 2;59(1):530-9. PMC3195954 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3195954/ Abstract Labels that identify specific anatomical and functional structures within medical images are essential to the characterization of...

The inaccuracies of the STAPLE model of rater behavior. A representative slice from the truth model is shown in (A). The expected STAPLE model of rater behavior can be seen in (B). STAPLE operates under the assumption that there is a uniform probability that any given rater would mis-label a given voxel. The observed model of rater behavior can be seen in (C). The primary difference between (B) and (C) is that the human raters showed a clear inclination to mislabel boundary pixels and other ambiguous regions.

Robust Statistical Label Fusion through Consensus Level, Labeler Accuracy and Truth Estimation (COLLATE)

Oct. 31, 2011—Andrew J. Asman and Bennett A. Landman. “Robust Statistical Label Fusion through Consensus Level, Labeler Accuracy and Truth Estimation (COLLATE)”, IEEE Transactions on Medical Imaging. 2011 October; 30(10): 1779–1794. PMC3150602 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3150602/ Abstract Segmentation and delineation of structures of interest in medical images is paramount to quantifying and characterizing structural, morphological, and functional...

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...

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”,...

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...

Simulations of free diffusion. Error between theoretical and simulated diffusion probability-density functions was minimized with 5–10 time-steps when the number of spins × the number of time-steps was held constant. Curves show mean error ± standard error for three diffusion times.

Complex Geometric Models of Diffusion and Relaxation in Healthy and Damaged White Matter

Jan. 31, 2010—. B. A. Landman, J. A.D. Farrell, S. A. Smith, D. Reich, P. Calabresi, and P. C.M. van Zijl, “Complex Geometric Models of Diffusion and Relaxation in Healthy and Damaged White Matter”, NMR in Biomed, Volume 23 Issue 2, Pages 152 – 162. (2010). PMC2838925 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2838925/ Abstract Which aspects of tissue microstructure...

Simulated impact of thresholds on bias in SVNF estimation. 5,000 Monte Carlo iterations were performed for each of 10 Rician distributed observations and for each of 40 linearly spaced SNRs between 1:2 and 20:1. For each dataset, the SVNF was estimated using Eq. (7) with a threshold of 0:1 (no threshold), 3:1, 5:1, and by Eq. (8) (data-adaptive threshold). Second, 5,000 Monte Carlo draws of 1 Rayleigh distributed observation (i.e., an “outlier” with a Rician distribution with SNR=0:1) and 9 Rician distributed observations for each of 40 linearly spaced SNRs between 1:2 and 20:1. For each dataset, the SVNF was estimated using Eq. (7) with a threshold of 0:1 (no threshold), 3:1, 5:1, and by Eq. (8) (data-adaptive threshold). The standard error on all reported estimates is less than 0.005 (for clarity, error bars are not shown).

Robust Estimation of Spatially Variable Noise Fields

Aug. 31, 2009—B. A. Landman, P-L Bazin, S. A. Smith, and J. L. Prince, “Robust Estimation of Spatially Variable Noise Fields”, Magnetic Resonance in Medicine, Aug;62(2):500-9. 2009 PMC2806192 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2806192/ Abstract Consideration of spatially variable noise fields is becoming increasing necessary in magnetic resonance imaging given recent innovations in artifact identification and statistically-driven image processing. Fast...

Simulation results. In the idealized simulation (left), the background estimator (BE) method performed better than other methods both in terms of mean squared error (first row) and variability (second row). In the simulation incorporating spatial variability and artifacts (right), the proposed spatially regularized robust (SRR) method greatly exceeded the performance of the standard approaches.

Estimation and application of spatially variable noise fields in diffusion tensor imaging.

Feb. 28, 2009—B. A. Landman, P-L Bazin, and J. L. Prince, “Estimation and Application of Spatially Variable Noise Fields in Diffusion Tensor Imaging”, Magnetic Resonance Imaging, Volume 27, Issue 6, Pages 741-751. (2009) PMC2733233 Full text: https://www.ncbi.nlm.nih.gov/pubmed/19250784 Abstract Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically...

Schematic of reliability estimation with experimental data (upper panel) and simulated data (lower panel).

Diffusion Tensor Imaging at Low SNR: Non-monotonic behaviors of tensor contrasts

Jul. 31, 2008—B. A. Landman, J. Farrell, H. Huang, J. Prince, and S. Mori. “Diffusion Tensor Imaging at Low SNR: Non-monotonic behaviors of tensor contrasts”, Magnetic Resonance Imaging. 26(6):790-800. July 2008 PMC2583784 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2583784/ Abstract Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in tissue micro-architecture in...

Minimum potential energy (PE) partitions of the Jones30 DW scheme. The optimal PE partitions (left) are evenly distributed as indicated by the shading which is proportional to the area of the spherical Voronoi tessellations of the DW directions. The realized directions are distinct (right) from the specified ones (left) because the gradient tables are corrected for subject motion. The right panel shows 30 clusters, where each cluster represents a specified DW direction and consists of three sub-clusters which represent realized DW directions from each session. The separation of the sub-clusters shows the inter-session effects, while the distribution of the symbols shows the intra-session effects. Large ovals indicate the subset of the Jones30 that was used to construct the PE6 partition.

Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T.

Apr. 30, 2007—B. A. Landman, J. A. Farrell, C. K. Jones, S. A. Smith, J. L. Prince, P. C. van Zijl, and S. Mori. “Effects of Diffusion Weighting Schemes on the Reproducibility of DTI-derived Fractional Anisotropy, Mean Diffusivity, and Principal Eigenvector Measurements at 1.5T”, NeuroImage. 36(4): 1123-1138. July 2007. PMID17532649 Full text: https://www.ncbi.nlm.nih.gov/pubmed/17532649 Abstract Diffusion tensor imaging...