‘optic nerve’

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...

An example of a healthy nerve (A) and an atrophied nerve (B) from the multi-atlas segmentation atlas subjects. In the coronal view, ON atrophy is apparent. Quantification of these structural differences is the target of the presented algorithm.

Disambiguating the Optic Nerve from the Surrounding Cerebrospinal Fluid: Application to MS-related Atrophy

Jan. 31, 2016—Robert L. Harrigan, Andrew J. Plassard, Frederick W. Bryan, Gabriela Caires, Louise A. Mawn, Lindsey M. Dethrage, Siddharama Pawate, Robert L. Galloway, Seth A. Smith, Bennett A. Landman. “Disambiguating the Optic Nerve from the Surrounding Cerebrospinal Fluid: Application to MS-related Atrophy.” Magnetic Resonance in Medicine. In press 2014.” Full Text: https://www.ncbi.nlm.nih.gov/pubmed/25754412 Abstract PURPOSE: Our goal...

Qualitative results for the optimal multi-atlas segmentation approach for seven subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2-D slice. The bottom rows show pointwise surface distance error of the label fusion estimate for the ONs and the eye globe structure. The proposed multi-atlas pipeline results in reasonably accurate segmentations for the ON structure. However, slight oversegmentations of the ONs can be observed in certain cases (subjects 4 and 7) supporting the results in the volumetry section

Robust Optic Nerve Segmentation on Clinically Acquired CT

Dec. 31, 2014—Robert Harrigan, Swetasudha Panda, Andrew J. Asman, Michael P. DeLisi, Benjamin C. W. Yvernault, Seth A. Smith, Robert L. Galloway, Louise A. Mawn, Bennett A. Landman “Robust Optic Nerve Segmentation on Clinically Acquired CT.” Journal of Medical Imaging. 1(3), 034006 (Dec 17, 2014). † PMC3903299 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/26158064 Abstract The optic nerve (ON) plays a...

Figure 3
Qualitative results for the optimal multi-atlas segmentation approach for 7 subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2D slice. The bottom rows show point-wise surface distance error of the label fusion estimate for the ONs and the eye globe structure. The proposed multi-atlas pipeline results in reasonably accurate segmentations for the ON structure. However, slight over segmentations of the ONs can be observed in certain cases (subjects 4 and 7).

Robust Optic Nerve Segmentation on Clinically Acquired CT

Feb. 1, 2014—Swetasudha Panda, Andrew J. Asman, Michael P. DeLis, Louise A. Mawn, Robert L. Galloway, Bennett A. Landman. “Robust Optic Nerve Segmentation on Clinically Acquired CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817810 Abstract The optic nerve is a sensitive central nervous system structure,...

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...