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Image Segmentation Category

Investigation of Bias in Continuous Medical Image Label Fusion

Jun. 30, 2016—Fangxu Xing; Jerry Prince; Bennett Landman. Investigation of Bias in Continuous Medical Image Label Fusion. PLoS One. 2016 Jun 3;11(6):e0155862. PMC4892597 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27258158 Abstract Image labeling is essential for analyzing morphometric features in medical imaging data. Labels can be obtained by either human interaction or automated segmentation algorithms, both of which suffer from errors....

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Consistent Cortical Reconstruction and Multi-atlas Brain Segmentation

Apr. 30, 2016—Yuankai Huo, Aaron Carass, Susan M. Resnick, Dzung L. Pham, Jerry L. Prince, Bennett A. Landman. “Consistent Cortical Reconstruction and Multi-atlas Brain Segmentation”. NeuroImage. Volume 138, September 2016, Pages 197–210 PMC4927397 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27184203 Abstract Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can...

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A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging.

Feb. 27, 2016—Yurui Gao, Prasanna Parvathaneni, Kurt G. Schilling, Zhoubing Xu, Ann S. Choe, Bennett A. Landman, Adam M. Anderson. A 3D high resolution ex vivo white matter atlas of the common squirrel monkey (Saimiri sciureus) based on diffusion tensor imaging. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text Abstract...

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Improving Cerebellar Segmentation with Statistical Fusion

Feb. 27, 2016—Andrew J. Plassard, Zhen Yang, Swati D. Rane, Jerry L. Prince, Daniel O. Claassen, Bennett A. Landman. “Improving Cerebellar Segmentation with Statistical Fusion. In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Improving+Cerebellar+Segmentation+with+Statistical+Fusion Abstract The cerebellum is a somatotopically organized central component of the central nervous system well known...

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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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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...

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Multi-atlas Learner Fusion: An efficient segmentation approach for large-scale data

Dec. 26, 2015—Andrew J. Asman, Yuankai Huo, Andrew J. Plassard, and Bennett A. Landman, “Multi-atlas Learner Fusion: An efficient segmentation approach for large-scale data”, Medical Image Analysis (MedIA), 2015 Dec;26(1):82-91. Full text: http://linkinghub.elsevier.com/retrieve/pii/S1361-8415(15)00135-8 Abstract We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on...

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Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol

Oct. 31, 2015—Zhoubing Xu, Andrew J. Asman, Rebeccah Baucom, Richard G Abramson, Benjamin K. Poulose, and Bennett A. Landman. “Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol.” PLoS ONE. 2015 Oct 28;10(10):e0141671. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/26509450 Abstract OBJECTIVE: We described and validated a quantitative anatomical labeling protocol for extracting clinically relevant quantitative parameters for...

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Efficient Multi-Atlas Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning

Aug. 31, 2015—Zhoubing Xu, Ryan P. Burke, Christopher P. Lee, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Efficient Multi-Atlas Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.” Medical Image Analysis. In press May 2015. † Full Text: http://www.medicalimageanalysisjournal.com/article/S1361-8415(15)00076-6/fulltext Abstract Abdominal segmentation on clinically acquired computed tomography (CT) has been a...

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