Informatics / Big Data Category
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...
Batch size: go big or go home? Counterintuitive improvement in medical autoencoders with smaller batch size
Nov. 13, 2022—Cailey I. Kerley*, Leon Y. Cai*, Yucheng Tang, Lori L. Beason-Held, Susan M. Resnick, Laurie E. Cutting, and Bennett A. Landman. *Equal first authorship Abstract Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders...
pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis
Jan. 12, 2022—Kerley, C.I., Chaganti, S., Nguyen, T.Q. et al. pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis. Neuroinform (2022). https://doi.org/10.1007/s12021-021-09553-4 Full text: NIHMSID, Springer Abstract Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering...
Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing
Oct. 26, 2018—Shunxing Bao, Prasanna Parvathaneni, Yuankai Huo, Yogesh Barve, Andrew J. Plassard, Yuang Yao, Hongyang Sun, Ilwoo Lyu, David H. Zald, Bennett A. Landman and Aniruddha Gokhale. “Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing.” Big Data (Big Data), 2018 IEEE International Conference. (accepted) (acceptance rate 18.9%) Full text: TBD Abstract Big data medical image processing...
Towards Portable Large-Scale Image Processing with High-Performance Computing
May. 8, 2018—Yuankai Huo, Justin Blaber, Stephen M. Damon, Brian D. Boyd, Shunxing Bao, Prasanna Parvathaneni, Camilo Bermudez Noguera, Shikha Chaganti, Vishwesh Nath, Greer M. Jasmine, Ilwoo Lyu, William R. French, Allen T. Newton, Baxter P. Rogers, Bennett A. Landman. “Towards Portable Large-Scale Image Processing with High-Performance Computing”. Journal of Digital Imaging. (2018): 1-11. Open Access Download...
Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service
Nov. 15, 2016—Shunxing Bao, Andrew Plassard, Bennett Landman and Aniruddha Gokhale. “Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service.” IEEE International Conference on Cloud Engineering (IC2E), Vancouver, Canada, April 2017. Full text: NIHMSID Abstract Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval....
Resource Estimation in High Performance Medical Image Computing
Oct. 31, 2014—Rueben Banalagay, Kelsie J. Covington, D.Mitch Wilkes, Bennett A. Landman. “Resource Estimation in High Performance Medical Image Computing.” Neuroinformatics. 2014 Oct;12(4):563-73. † PMC4381797 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24906466 Abstract Medical imaging analysis processes often involve the concatenation of many steps (e.g., multi-stage scripts) to integrate and realize advancements from image acquisition, image processing, and computational analysis. With the...
![DICOM can be converted into research file formats viewed with SPM[7], MIPAV[8]. 3D Slicer[9]. MRIcron[10], MATLAB (Mathworks, Natick, MA)](https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2304/2016/10/14140904/nihms671205f1.jpg)
Validating DICOM transcoding with an open multi-format resource
Oct. 31, 2014—Benjamin C. Yvernault, Charles D. Theobald, Jr., Jolinda C. Smith, Victoria Villalta, David H. Zald, Bennett A. Landman. “Validating DICOM transcoding with an open multi-format resource.” Neuroinformatics. 2014 Oct;12(4):615-7. † PMC4391369 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24777387 Abstract The Digital Imaging and Communications in Medicine (DICOM) standard has allowed wide-scale interoperability between medical imaging devices allowed for construction...
Self-Assessed Performance Improves Statistical Fusion of Image Labels.
Feb. 15, 2014—Frederick W. Bryan, Zhoubing Xu, Andrew J. Asman, Wade M. Allen, Daniel S. Reich, and Bennett A. Landman. “Self-Assessed Performance Improves Statistical Fusion of Image Labels.” Medical Physics. 2014 Mar;41(3):031903. PMC24593721† Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3978333/ Abstract Purpose: Expert manual labeling is the gold standard for image segmentation, but this process is difficult, time-consuming, and prone...
Texture Analysis Improves Level Set Segmentation of the Anterior Abdominal Wall.
Dec. 15, 2013—Zhoubing Xu, Wade M. Allen, Rebeccah B. Baucom, Benjamin K. Poulose, Bennett A. Landman. “Texture Analysis Improves Level Set Segmentation of the Anterior Abdominal Wall.” Medical Physics. 2013 Dec;40(12):121901. † PMC3838426 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24320512 Abstract PURPOSE: The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is...