A framework for scalable biophysics-based image analysis

Amir Gholami, Andreas Mang, Klaudius Scheufele, Christos Davatzikos, Miriam Mehl, George Biros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a framework for coupling biophysical models with medical image analysis. It provides solvers for an image-driven inverse brain tumor growth model and an image registration problem, the combination of which can eventually help in diagnosis and prognosis of brain tumors. The two main computational kernels of SIBIA are a Fast Fourier Transformation (FFT) implemented in the library AccFFT to discretize differential operators, and a cubic interpolation kernel for semi-Lagrangian based advection. We present efficiency and scalability results for the computational kernels, the inverse tumor solver and image registration on two x86 systems, Lonestar 5 at the Texas Advanced Computing Center and Hazel Hen at the Stuttgart High Performance Computing Center. We showcase results that demonstrate that our solver can be used to solve registration problems of unprecedented scale, 40963 resulting in ∼ 200 billion unknowns - a problem size that is 64x larger than the state-of-the-art. For problem sizes of clinical interest, SIBIA is about 8x faster than the state-of-the-art.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450351140
DOIs
StatePublished - Nov 12 2017
EventInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 - Denver, United States
Duration: Nov 12 2017Nov 17 2017

Other

OtherInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
CountryUnited States
CityDenver
Period11/12/1711/17/17

Fingerprint

Biophysics
Image analysis
Tumors
Image registration
Brain
Advection
Mathematical operators
Scalability
Interpolation

Keywords

  • Bio-Physics based image analysis
  • Scalable image registration

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Gholami, A., Mang, A., Scheufele, K., Davatzikos, C., Mehl, M., & Biros, G. (2017). A framework for scalable biophysics-based image analysis. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 [19] Association for Computing Machinery, Inc. https://doi.org/10.1145/3126908.3126930

A framework for scalable biophysics-based image analysis. / Gholami, Amir; Mang, Andreas; Scheufele, Klaudius; Davatzikos, Christos; Mehl, Miriam; Biros, George.

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017. Association for Computing Machinery, Inc, 2017. 19.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gholami, A, Mang, A, Scheufele, K, Davatzikos, C, Mehl, M & Biros, G 2017, A framework for scalable biophysics-based image analysis. in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017., 19, Association for Computing Machinery, Inc, International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017, Denver, United States, 11/12/17. https://doi.org/10.1145/3126908.3126930
Gholami A, Mang A, Scheufele K, Davatzikos C, Mehl M, Biros G. A framework for scalable biophysics-based image analysis. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017. Association for Computing Machinery, Inc. 2017. 19 https://doi.org/10.1145/3126908.3126930
Gholami, Amir ; Mang, Andreas ; Scheufele, Klaudius ; Davatzikos, Christos ; Mehl, Miriam ; Biros, George. / A framework for scalable biophysics-based image analysis. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017. Association for Computing Machinery, Inc, 2017.
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