Cloud Computing 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...
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,...
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...
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....
![Hadoop and SGE data retrieval, processing and storage working flow basing on Multi-atlas CRUISE (MaCRUISE) segmentation [14, 15]. The data in an HBase table is approximately balanced to each Regionserver. The Regionserver collocates with a Hadoop Datanode to fully utilize the data collocation and locality[7]. We design our proposed computation models using only the map phase of Hadoop’s MapReduce [13]. In this phase, the data is retrieved locally; if the result were moved to reduce phase, more data movement would occur, because the reduce phase does not ensure process local data. Within the map phase, all necessary data is retrieved and saved on a local directory and gets furtherly processed by locally installed binary executables command-line program. After that, the results of processing are uploaded back to HBase. For SGE, the user submits a batch of jobs to a submit host, and this host dispatches the job to execution hosts. Each execution host retrieves the data within a shared NFS and stores the result back to the NFS.](https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2304/2016/11/14140802/Screen-Shot-2017-02-08-at-1.11.13-PM.png)
Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine
Nov. 1, 2016—Shunxing Bao, Frederick D. Weitendorf, Andrew J. Plassard, Yuankai Huo, Aniruddha Gokhale, Bennett A. Landman. “Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine”. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Traditional large scale processing uses a cluster computer that combines a group of workstation nodes...
DAX – The Next Generation: Towards One Million Processes on Commodity Hardware
Nov. 1, 2016—Stephen M. Damon, Brian D. Boyd, Andrew J. Plassard, Warren Taylor, Bennett A. Landman. “DAX – The Next Generation: Towards One Million Processes on Commodity Hardware” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. Full Text: Abstract Large-scale image processing demands a standardized way of storage and distribution. The...
Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services
Feb. 27, 2016—Shunxing Bao, Stephen M. Damon, Bennett A. Landman, Aniruddha Gokhale. “Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Performance+Management+of+High+Performance+Computing+for+Medical+Image+Processing+in+Amazon+Web+Services Abstract Adopting high performance cloud computing for medical image processing is a popular...