Author

Imaging Biomarkers in Thyroid Eye Disease and their Clinical Associations

Dec. 18, 2018—Shikha Chaganti, Katrina Nelson, Kevin Mundy, Robert Harrigan, Robert Galloway, Louise A. Mawn, Bennett Landman, “Imaging biomarkers in thyroid eye disease and their clinical associations,” Journal of Medical Imaging. (2018), doi: 10.1117/1.JMI.5.4.044001. Abstract Purpose: The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multi-atlas...

Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing

Dec. 17, 2018—Shikha Chaganti, Louise A. Mawn, Hakmook Kang, Josephine Egan, Susan M. Resnick, Lori L. Beason-Held, Bennett A. Landman, Thomas A. Lasko. “Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing”. IEEE Journal of Biomedical and Health Informatics. (In Press) Abstract Composite models that combine medical imaging with electronic medical records (EMR) improve...

Structural Functional Associations of the Orbit in Thyroid Eye Disease: Kalman Filters to Track Extraocular Muscles

Feb. 10, 2016—Shikha Chaganti, Katrina Nelson, Kevin Mundy, Yifu Luo, Robert L. Harrigan, Steve Damon, Daniel Fabbri, Louise Mawn, Bennett Landman. “Structural Functional Associations of the Orbit in Thyroid Eye Disease: Kalman Filters to Track Extraocular Muscles”. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845964/ Abstract Pathologies of...

Short Term Reproducibility of a High Contrast 3-D Isotropic Optic Nerve Imaging Sequence in Healthy Controls

Feb. 1, 2016—Robert L. Harrigan, Alex K. Smith, Louise A. Mawn, Seth A. Smith, Bennett A. Landman. “Short Term Reproducibility of a High Contrast 3-D Isotropic Optic Nerve Imaging Sequence in Healthy Controls.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4862660/?report=classic Abstract The optic nerve (ON) plays a...

Whole Abdominal Wall Segmentation using Augmented Active Shape Models (AASM) with Multi-Atlas Label Fusion and Level Set

Feb. 1, 2016—Zhoubing Xu, Rebeccah B. Baucom, Richard G. Abramson, Benjamin K. Poulose, Bennett A. Landman, “Whole Abdominal Wall Segmentation using Augmented Active Shape Models (AASM) with Multi-Atlas Label Fusion and Level Set”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845968/ Abstract The abdominal wall is an...

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...

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...

Representative segmentations for two subjects. Labels: red: volumetric hernia, yellow: outer abdominal wall (axial), blue: inner abdominal wall (axial), green: outer abdominal wall (sagittal), pink: inner abdominal wall (sagittal), cyan: skin

Quantitative Anatomical Labeling of the Anterior Abdominal Wall

Feb. 1, 2013—Wade M. Allen, Zhoubing Xu, Andrew J. Asman, Benjamin K. Poulose, Bennett A. Landman. “Quantitative Anatomical Labeling of the Anterior Abdominal Wall.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903294/?report=classic Abstract Ventral hernias (VHs) are abnormal openings in the anterior abdominal wall that are common...

Flowchart demonstrating the PACS-based Diffusion Tensor Imaging quality analysis framework. The framework begins by utilizing a PACS-based framework for acquiring a recently scanned target. Once the DTI image is acquired, standard pre-processing steps are performed for accounting for patient movement via an intra-subject rigid registration. Next, a whole image FA map and the multi-atlas segmentation process (using the pre-computed B0 atlases) are performed simultaneously in order to achieve the pertinent information about the current subject. Finally, structure-wise FA noise estimates are constructed using a wild-bootstrap estimation procedure.

Robust Inter-Modality Multi-Atlas Segmentation for PACS-based DTI Quality Control

Feb. 1, 2013—Andrew J. Asman, Carolyn B. Lauzon, Bennett A. Landman. “Robust Inter-Modality Multi-Atlas Segmentation for PACS-based DTI Quality Control.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874255/ Abstract Anatomical contexts (spatial labels) are critical for interpretation of medical imaging content. Numerous approaches have been devised for...

Segmentation results for four subjects. The first column represents the results of bone skeleton and skin. The second column demonstrates the segmentation of outer abdominal wall (green) overlaid with grids of labeled ground truth (red). The third to the sixth columns show the intra- and inter- variability of abdominal wall over slices and the segmentation results of our approach (red).

Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study

Feb. 1, 2013—Zhoubing Xu, Wade M. Allen, Benjamin K. Poulose, Bennett A. Landman. “Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013† Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877250/?report=classic Abstract The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of...

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