Big Data Category
Reproducibility evaluation of the effects of MRI defacing on brain segmentation
Nov. 10, 2023—Chenyu Gao, Bennett A. Landman, Jerry L. Prince, Aaron Carass. “Reproducibility evaluation of the effects of MRI defacing on brain segmentation”. J. Med. Imag. 10(6), 064001 (2023), https://doi.org/10.1117/1.JMI.10.6.064001, [PDF] Abstract Purpose Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing...
Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis
Sep. 1, 2023—Shunxing Bao, Brian D Boyd, Praitayini Kanakaraj, Karthik Ramadass, Francisco A. C. Meyer, Yuqian Liu, William E. Duett, Yuankai Huo, Ilwoo Lyu, David H. Zald, Seth A. Smith, Baxter P. Rogers, Bennett A. Landman. Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis. Journal of Digital Imaging. 2022 Full Text Abstract...
Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions
Aug. 31, 2023—Kurt G Schilling, Derek Archer, Francois Rheault, Ilwoo Lyu, Yuankai Huo, Leon Y Cai, Silvia A Bunge, Kevin S Weiner, John C Gore, Adam W Anderson, Bennett A Landman Paper: https://pubmed.ncbi.nlm.nih.gov/36817413/ Abstract It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter...
Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features
Aug. 31, 2023—Kurt G Schilling, Derek Archer, Francois Rheault, Ilwoo Lyu, Yuankai Huo, Leon Y Cai, Silvia A Bunge, Kevin S Weiner, John C Gore, Adam W Anderson, Bennett A Landman Paper: https://pubmed.ncbi.nlm.nih.gov/37074446 Abstract Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up...
Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective. Medical Image Analysis
Aug. 31, 2023—Kaiwen Xu, Thomas Li, Mirza S. Khan, Riqiang Gao, Sanja L. Antic, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman Paper: https://www.sciencedirect.com/science/article/pii/S1361841523001123 Code: https://github.com/MASILab/S-EFOV Abstract Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures...
AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection
Aug. 31, 2023—Kaiwen Xu, Mirza S. Khan, Thomas Z. Li, Riqiang Gao, James G. Terry, Yuankai Huo, Thomas A. Lasko, John Jeffrey Carr, Fabien Maldonado, Bennett A. Landman, Kim L. Sandler Paper: https://pubs.rsna.org/doi/epdf/10.1148/radiol.222937 Abstract Background An artificial intelligence (AI) algorithm has been developed for fully automated body composition assessment of lung cancer screening noncontrast low-dose CT of the...
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Aug. 31, 2023—Xin Yu, Qi Yang, Yinchi Zhou, Leon Y. Cai , Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Zizhao Zhang, Yuankai Huo, Bennett A. Landman, Yucheng Tang Paper: https://arxiv.org/abs/2209.14378 Code: https://github.com/Project-MONAI/model-zoo/tree/dev/models Abstract Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional repre- sentation learning capabilities...
pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations
Aug. 31, 2023—Cailey I. Kerley, Karthik Ramadass, Tin Q. Nguyen, Laurie E. Cutting, Bennett A. Landman, Matthew Berger Paper: https://pubmed.ncbi.nlm.nih.gov/37021295/ Code: https://github.com/MASILab/pyPheWAS Abstract Objective To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR). Materials and Methods Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to...
Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels
Dec. 1, 2022—Lucas W. Remedios, Shunxing Bao, Cailey I. Kerley, Leon Y. Cai, François Rheault, Ruining Deng, Can Cui, Sophie Chiron, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2023). Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels....
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Jul. 25, 2022—Tang, Yucheng, Dong Yang, Wenqi Li, Holger R. Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. “Self-supervised pre-training of swin transformers for 3d medical image analysis.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730-20740. 2022. Full text: Abstract Vision Transformers (ViT)s have shown great performance in self-supervised...