Improving Spleen Volume Estimation via Computer Assisted Segmentation on Clinically Acquired CT Scans
Oct. 31, 2016—Zhoubing Xu, Adam L. Gertz, Ryan P. Burke, Neil K. Bansal, Hakmook Kang, Bennett A. Landman, Richard G. Abramson. Improving Spleen Volume Estimation via Computer Assisted Segmentation on Clinically Acquired CT Scans. Acad Radiol. 2016 Oct;23(10):1214-20. PMC5026951 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27519156 Abstract Objectives: Multi-atlas fusion is a promising approach for computer-assisted segmentation of anatomic structures. The...
Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion
Oct. 31, 2016—Yuankai Huo, Andrew J. Asman, Andrew J. Plassard, Bennett A. Landman. “Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion.” Human Brain Mapping. In Press October 2016 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27726243 Abstract Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of...
Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI
Oct. 30, 2016—Yuankai Huo, Katherine Aboud, Hakmook Kang, Laurie E. Cutting, Bennett A. Landman. “Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI”. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, October 2016. Oral Presentation. Full text: NIHMSID 826509 Abstract Understanding brain volumetry is essential to understand neurodevelopment...
Can We Get Around the Crossing Fiber Problem by Increasing Spatial resolution?
Sep. 1, 2016—Kurt G. Schilling, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Bennett A. Landman, Adam W Anderson. “Can We Get Around the Crossing Fiber Problem by Increasing Spatial resolution?” In Proceedings of the ISMRM Workshop on Diffusion MRI. Lisbon, Portugal, September 2016. Oral presentation. Full text: PDF Abstract It is now widely recognized that voxels with crossing...
Reproducibility and Variation of Diffusion Measures in the Squirrel Monkey Brain, In Vivo and Ex Vivo
Aug. 31, 2016—Kurt Schilling, Yurui Gao, Iwona Stepniewska, Ann S. Choe, Bennett A. Landman, and Adam W Anderson. “Reproducibility and Variation of Diffusion Measures in the Squirrel Monkey Brain, In Vivo and Ex Vivo”. Magnetic Resonance Imaging. In press August 2015 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27587226 Abstract Purpose: Animal models are needed to better understand the relationship between diffusion...
Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.
Jul. 31, 2016—Christopher E. Gonzalez, Vijay K. Venkatraman, Yang An, Bennett A. Landman, Christos Davatzikos, Veera Venkata Ratnam Bandaru, Norman J. Haughey, Luigi Ferruci, Michelle M. Mielke, Susan M. Resnick. “Peripheral sphingolipids are associated with variation in white matter microstructure in older adults.” Neurobiology of Aging. July 2016. Volume 43, Pages 156–163 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/27255825 Abstract Sphingolipids...
Deep Learning for Brain Tumor Classification
Jul. 1, 2016—Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri. “Deep Learning for Brain Tumor Classification.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Abstract Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is...
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....
Evaluation of Five Registration Methods for the Human Abdomen on Clinically Acquired CT
Jun. 30, 2016—Zhoubing Xu, Christopher P. Lee, Marc Modat, Daniel Rueckert, Sebastien Ourselin, Richard G. Abramson, Bennett A. Landman. “Evaluation of Five Registration Methods for the Human Abdomen on Clinically Acquired CT”. IEEE Transactions of Biomedical Engineering. 2016 Aug;63(8):1563-72. PMC4972188 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27254856 Abstract Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal...
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...