Medical-image Analysis and Statistical Interpretation (MASI) Lab

Figure 2
Results from the controlled rater observation simulation. The target intensity image and the corresponding true labels can be seen (A) and (B), respectively. The set of input observations used in this experiment can be seen in (C). The results of the four algorithms, Majority Vote, STAPLE, Binary-COLLATE and Multi-COLLATE can be seen in (D)–(G). Note that the 3 consensus-level implementation of Multi-COLLATE is the only algorithm that gets the correct answer. The 3 energy in each of the consensus levels can be seen in (H)–(J). Note that by distributing the non-consensus voxels into two separate levels, (H) and (I), the estimation process converges to the correct answer.

Generalized Statistical Label Fusion using Multiple Consensus Levels

Feb. 1, 2012—Z. Xu, A. Asman and B. Landman. “Generalized Statistical Label Fusion using Multiple Consensus Levels.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) PMC3438516† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=%E2%80%9CGeneralized+Statistical+Label+Fusion+using+Multiple+Consensus+Levels. Abstract Segmentation plays a critical role in exposing connections between biological structure and function. The process of label fusion collects and...

Simultaneous Segmentation and Statistical Label Fusion.

Feb. 1, 2012—A. Asman and B. Landman. “Simultaneous Segmentation and Statistical Label Fusion.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24357909 Abstract Labeling or segmentation of structures of interest in medical imaging plays an essential role in both clinical and scientific understanding. Two of the common...

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...

Flow chart of steps to create SIMEX Estimated Bias and True Bias maps. Variables refer to terms defined explicitly by equations in the Theory section.

Assessment of bias for MRI diffusion tensor imaging using SIMEX

Sep. 30, 2011—Carolyn B. Lauzon, Andrew Asman, Ciprian Crainiceanu, Brian Caffo, Bennett A. Landman. “Assessment of Bias for MRI Diffusion Tensor Imaging Using SIMEX”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Toronto, Canada, September 2011 (30% acceptance rate) NIHMS ID 301343 Full text: https://www.ncbi.nlm.nih.gov/pubmed/21995019 Abstract Diffusion Tensor Imaging (DTI) is a Magnetic Resonance...

Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging

Sep. 1, 2011—Xue Yang, Carolyn B. Lauzon, Ciprian Crainiceanu, Brian Caffo, Susan M. Resnick, Bennett A. Landman. “Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging”, In MICCAI 2011 Workshop of Multi-Modal Brain Image Analysis. Toronto, Canada, September 2011 (Oral Presentation) NIHMS317653 *** BEST PAPER AWARD *** Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208720/ Abstract Massively univariate regression and inference in...

On the Application of Human Rater Models to Statistical Fusion in Multi-Atlas Labeling

Sep. 1, 2011—A. Asman, Antong Chen, and B. Landman. “On the Application of Human Rater Models to Statistical Fusion in Multi-Atlas Labeling.” In MICCAI 2011 Workshop on Multi-Atlas Methods and Statistical Fusion. Toronto, Canada, September 2011 (Oral Presentation) NIHMS317651 Full text: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.3272&rep=rep1&type=pdf Abstract Segmentation is critical to understanding the complex relationships between biological structure and function. Statistical algorithms...

Estimation of Registration Accuracy Applied to Multi-Atlas Segmentation

Sep. 1, 2011—R. Datteri, A. Asman, B. Landman, and B. Dawant. “Estimation of Registration Accuracy Applied to Multi-Atlas Segmentation.” In . Toronto, Canada, September 2011 (Oral Presentation) Full text: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.728.4212&rep=rep1&type=pdf Abstract Multi-atlas registration-based segmentation has recently become a popular technique in medical imaging. Since the quality of individual atlas segmentations affect the quality of the results, atlas selection...

$Example potential application of HUBRIS for the reconstruction of improved diffusion tensor imaging (DTI) fractional anisotropy (FA) maps using a multi-shot acquisition. The presented color maps from single-shot (a) and multi-shot (b) echo-planar imaging (EPI) data show multi-shot EPI exhibits reduced blurring and geometric distortions. The generation of the improved images requires a custom reconstruction algorithm that can be accessed by HUBRIS.$

“Integrating Medical Imaging Analyses through a High-throughput Bundled Resource Imaging System”

Aug. 11, 2011—Kelsie Covington, E. Brian Welch, Bennett A. Landman. “Integrating Medical Imaging Analyses through a High-throughput Bundled Resource Imaging System”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 (Oral Presentation) PMC3154704† Full text Abstract Exploitation of advanced, PACS-centric image analysis and interpretation pipelines provides well-developed storage, retrieval, and archival capabilities...

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...

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