Performance management of high performance computing for medical image processing in Amazon Web Services

Shunxing Bao; Stephen M. Damon; Bennett A. Landman; Aniruddha Gokhale

doi:10.1117/12.2217396

25 March 2016 Performance management of high performance computing for medical image processing in Amazon Web Services

Shunxing Bao, Stephen M. Damon, Bennett A. Landman, Aniruddha Gokhale

Proceedings Volume 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations; 97890Q (2016) https://doi.org/10.1117/12.2217396
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical- Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for- use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline.

Citation Download Citation

Shunxing Bao, Stephen M. Damon, Bennett A. Landman, and Aniruddha Gokhale "Performance management of high performance computing for medical image processing in Amazon Web Services", Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890Q (25 March 2016); https://doi.org/10.1117/12.2217396

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