‘Multi-Atlas Segmentation’

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...

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...

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...

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...