‘Non-Local STAPLE’

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...

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...