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Medical-image Analysis and Statistical Interpretation Lab

‘Computed Tomography’

learning from dispersed labels

Learning from dispersed manual annotations with an optimized data weighting policy

Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Yunqiang Chen, Dashan Gao, Michael R. Savona, Richard G. Abramson, Shunxing Bao, Yuankai Huo and Bennett A. Landman, “Learning from Dispersed Manual Annotations with an Optimized Data Weighting Policy”, Journal of Medical Imaging, 2020. Full Text: Abstract https://pubmed.ncbi.nlm.nih.gov/32775501/ Purpose: Deep learning methods have become essential tools for quantitative interpretation of medical...

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A cross-platform informatics system for the Gut Cell Atlas: integrating from clinical, anatomical and histological data

Dec. 6, 2020—Shunxing Bao, Sophie Chiron, Yucheng Tang, Cody N. Heiser, Austin N. Southard-Smith, Ho Hin Lee, Marisol A. Ramirez, Yuankai Huo, Mary K. Washington, Elizabeth A. Scoville, Joseph T. Roland, Qi Liu, Ken S. Lau, Keith T. Wilson, Lori A. Coburn, Bennett A. Landman, A cross-platform informatics system for the Gut Cell Atlas: integrating from clinical,...

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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...

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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...

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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...

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