Neuroimaging Category

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...

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...

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...

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...

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 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...

« Previous Page