Medical-image Analysis and Statistical Interpretation (MASI) Lab

Hierarchical representation of rater performance. A hierarchical model is developed for the brain, where, at each level, the performance of a rater is quantified. The overall quality of rater is then estimated through the unified hierarchical performance model.

Statistical label fusion with hierarchical performance models

Feb. 1, 2014—Andrew J. Asman, Alexander S. Dagley, Bennett A. Landman. “Statistical label fusion with hierarchical performance models.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014 Andrew J. Asman, Alexander S. Dagley, and Bennett A. Landman. Oral Presentation. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4013116/ Abstract Label fusion is a critical step in many image segmentation...

Texture Analysis Improves Level Set Segmentation of the Anterior Abdominal Wall.

Dec. 15, 2013—Zhoubing Xu, Wade M. Allen, Rebeccah B. Baucom, Benjamin K. Poulose, Bennett A. Landman. “Texture Analysis Improves Level Set Segmentation of the Anterior Abdominal Wall.” Medical Physics. 2013 Dec;40(12):121901. † PMC3838426 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24320512 Abstract PURPOSE: The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is...

Flowchart of the proposed iterative Non-Local STAPLE (iNLS) multi-atlas framework. Using an iterative atlas-target registration refinement framework, we expand the range of anatomical variability that can be reliably segmented.

Robust GM/WM Segmentation of the Spinal Cord with Iterative Non-Local Statistical Fusion

Oct. 30, 2013—Andrew J. Asman, Seth A. Smith, Daniel Reich, and Bennett A. Landman. “Robust GM/WM Segmentation of the Spinal Cord with Iterative Non-Local Statistical Fusion”, In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2013 Nagoya, Japan Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918679/ Abstract New magnetic resonance imaging (MRI) sequences are enabling clinical study of...

Qualitative results for the detection of malignant gliomas. Five representative examples are presented. For each example, the target volume, expert labeling, label fusion estimate, and the out-of-atlas likelihood are presented. The first four examples represent cases where the tumor region is correctly identified. The last example represents the outlier case [seen in Fig. Fig.2c]2c] in which the cancerous region was almost completely missed.

Out-of-Atlas Likelihood Estimation using Multi-Atlas Segmentation.

Apr. 15, 2013—Andrew J. Asman, Lola Chambless, Reid Thompson, and Bennett A. Landman. “Out-of-Atlas Likelihood Estimation using Multi-Atlas Segmentation.” Medical Physics. 2013 Apr;40(4) PMC23556928 † Full-Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3625241/ Abstract Purpose: Multi-atlas segmentation has been shown to be highly robust and accurate across an extraordinary range of potential applications. However, it is limited to the segmentation of structures...

Each of the 567 DTI datasets was characterized by a 112 element vector of stored outputs from the pipeline. PCA analysis was performed on the resulting data. (A) DTI dataset locations in the first two dimensions of the PCA analysis. Data is symbolized by study Roman numeral (Table 1). Single arrow points to a data quality outlier from study I; subject 3 in Figure 4 and in (B). A double headed arrow points to a cluster representing an isolated protocol sub-group from study VI. (B) FA maps from similar sagittal slice locations in two subjects from Study I. Subject 4 is the indicated outlier in (A) and subject 5 was selected from the center of the study I cluster seen in (A).

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging.

Apr. 15, 2013—Carolyn B. Lauzon, Andrew J. Asman, Michael L. Esparza, Scott S. Burns, Qiuyun Fan, Yurui Gao, Adam W. Anderson, Nicole Davis, Laurie E. Cutting, Bennett A. Landman. “Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging.” PLoS ONE. 2013 Apr 30;8(4) PMC23637895 † Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640065/ Abstract Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural...

Assessment of Bias in MRI Diffusion Tensor Imaging Parameters Using SIMEX

Mar. 2, 2013—Carolyn B. Lauzon, Ciprian Crainiceanu, Brian C. Caffo, Bennett A. Landman, “Assessment of Bias in MRI Diffusion Tensor Imaging Parameters Using SIMEX”, Magnetic Resonance in Medicine. 2013 Mar 1;69(3):891-902. PMC22611000 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/22611000 Abstract: Diffusion tensor imaging enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion barriers at the...

Correcting Power and p-Value Calculations for Bias in Diffusion Tensor Imaging.

Mar. 2, 2013—Carolyn B. Lauzon and Bennett A. Landman, “Correcting Power and p-Value Calculations for Bias in Diffusion Tensor Imaging.” Magnetic Resonance Imaging. 2013 Mar 2. pii: S0730-725X(13) PMC23465764 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Correcting+Power+and+p-Value+Calculations+for+Bias+in+Diffusion+Tensor+Imaging. Abstract: Diffusion tensor imaging (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and...

Non-Local Statistical Label Fusion for Multi-Atlas Segmentation.

Feb. 17, 2013—Andrew J. Asman and Bennett A. Landman. “Non-Local Statistical Label Fusion for Multi-Atlas Segmentation.” Medical Image Analysis (MEDIA). 2013. 17(2):194-208. PMC23265798 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/23265798 Abstract: Multi-atlas segmentation provides a general purpose, fully-automated approach for transferring spatial information from an existing dataset (“atlases”) to a previously unseen context (“target”) through image registration. The method to...

Illustrations of the proposed system in use. (a) Surgeon using the system. (b) Virtual hand interacting with 3D abdominal model. (c) Navigation of axial slices. (d) Navigation of sagittal slices.

Immersive Virtual Reality for Visualization of Abdominal CT.

Feb. 15, 2013—Qiufeng Lin, Zhoubing Xu, Rebeccah Baucom, Benjamin Poulose, Bennett A. Landman, Robert E. Bodenheimer. “Immersive Virtual Reality for Visualization of Abdominal CT.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013† Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877248/ Abstract Immersive virtual environments use a stereoscopic head-mounted display and data glove to create high fidelity virtual experiences...

Overview of the integrated system. Multi-modal DICOM scans (upper left) are pushed to the XNAT/PACS server (upper right) via dcm4che tools. Verified DICOM scans are pulled and distributed to the cluster (bottom right) for further conversion, processing, and analysis by PyXNAT package scripts. The output results are pushed back for storage on XNAT. The online database REDCap (bottom left) provides/stores related information to/from XNAT. Black arrows indicate the directions of the data flow.

Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis

Feb. 1, 2013—Yurui Gao, Scott S. Burns, Carolyn B. Lauzon, Andrew Fong, David A. Twillie, Michael Wirt, Marc A. Zola, Bret W. Logan, Adam W. Anderson, Bennett A. Landman. “Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † Full...

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