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Computational Anatomy Gateway: Leveraging XSEDE Computational Resources for Shape Analysis

Published: 13 July 2014 Publication History

Abstract

Computational Anatomy (CA) is a discipline focused on the quantitative analysis of the variability in biological shape. The Large Deformation Diffeomorphic Metric Mapping (LDDMM) is the key algorithm which assigns computable descriptors of anatomical shapes and a metric distance between shapes. This is achieved by describing populations of anatomical shapes as a group of diffeomorphic transformations applied to a template, and using a metric on the space of diffeomorphisms. LDDMM is being used extensively in the neuroimaging (www.mristudio.org) and cardiovascular imaging (www.cvrgrid.org) communities. There are two major components involved in shape analysis using this paradigm. First is the estimation of the template, and second is calculating the diffeomorphisms mapping the template to each subject in the population. Template estimation is a computationally expensive problem, which involves an iterative process, where each iteration calculates one diffeomorphism for each target. These can be calculated in parallel and independently of each other, and XSEDE is providing the resources, in particular those provided by the cluster Stampede, that make these computations for large populations possible. Mappings from the estimated template to each subject can also be run in parallel. In addition, the use of NVIDIA Tesla GPUs available on Stampede present the possibility of speeding up certain convolution-like calculations which lend themselves well to the General Purpose GPU computation model. We are also exploring the use of the available Xeon Phi Co-processors to increase the efficiency of our codes. This will have a huge impact on both the neuroimaging and cardiac imaging communities as we bring these shape analysis tools online for use by these communities through our webservice (www.mricloud.org), with the XSEDE Computational Anatomy Gateway providing the resources to handle the computational demands for large populations.

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Cited By

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  • (2019)Expanding the Computational Anatomy Gateway from clinical imaging to basic neuroscience researchPractice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)10.1145/3332186.3332217(1-6)Online publication date: 28-Jul-2019
  • (2017)Performance of Image Matching in the Computational Anatomy GatewayPractice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact10.1145/3093338.3093361(1-7)Online publication date: 9-Jul-2017
  • (2017)Unbiased Diffeomorphic Mapping of Longitudinal Data with Simultaneous Subject Specific Template EstimationGraphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics10.1007/978-3-319-67675-3_12(125-136)Online publication date: 8-Sep-2017
  • Show More Cited By

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Published In

cover image ACM Other conferences
XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment
July 2014
445 pages
ISBN:9781450328937
DOI:10.1145/2616498
  • General Chair:
  • Scott Lathrop,
  • Program Chair:
  • Jay Alameda
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • NSF: National Science Foundation
  • Drexel University
  • Indiana University: Indiana University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2014

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Author Tags

  1. CUDA
  2. GPU
  3. MPI
  4. Xeon Phi
  5. cardiac imaging
  6. computational anatomy
  7. neuroscience
  8. shape analysis

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  • Research-article
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  • Refereed limited

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XSEDE '14

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XSEDE '14 Paper Acceptance Rate 80 of 120 submissions, 67%;
Overall Acceptance Rate 129 of 190 submissions, 68%

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Cited By

View all
  • (2019)Expanding the Computational Anatomy Gateway from clinical imaging to basic neuroscience researchPractice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)10.1145/3332186.3332217(1-6)Online publication date: 28-Jul-2019
  • (2017)Performance of Image Matching in the Computational Anatomy GatewayPractice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact10.1145/3093338.3093361(1-7)Online publication date: 9-Jul-2017
  • (2017)Unbiased Diffeomorphic Mapping of Longitudinal Data with Simultaneous Subject Specific Template EstimationGraphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics10.1007/978-3-319-67675-3_12(125-136)Online publication date: 8-Sep-2017
  • (2016)Tools for studying populations and timeseries of neuroanatomy enabled through GPU acceleration in the Computational Anatomy GatewayProceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale10.1145/2949550.2949574(1-7)Online publication date: 17-Jul-2016
  • (2016)Metamorphosis of images in reproducing kernel Hilbert spacesAdvances in Computational Mathematics10.1007/s10444-015-9435-y42:3(573-603)Online publication date: 1-Jun-2016

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