February, 2013

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...

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...

Flowchart demonstrating the proposed registration procedure. As the volumes used in this dataset are wildly varying in terms of field-of-view and voxel resolution, a robust registration procedure is needed. Our model begins by rigidly registering the bone structure of the target and the atlas images (achieved through thresholding). Using these rigid registrations the optic nerve centroids are estimated on the target image in order to estimate an appropriate region-of-interest. Using this consistent region-of-interest, consistent non-rigid registration can then be performed in order to achieve the highest possible correspondence between the targets and the atlases.

Robust Non-Local Multi-Atlas Segmentation of the Optic Nerve

Feb. 1, 2013—A. Asman, Michael P. DeLisi, Louise A. Mawn, Robert L. Galloway, and B. Landman. “Robust Non-Local Multi-Atlas Segmentation of the Optic Nerve.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † PMCID 24478826 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903299/?report=classic Abstract Labeling or segmentation of structures of interest on medical images plays an...