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Proceedings Paper

GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications
Author(s): Parag Bhosale; Marius Staring; Zaid Al-Ars; Floris F. Berendsen
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Paper Abstract

Currently, non-rigid image registration algorithms are too computationally intensive to use in time-critical applications. Existing implementations that focus on speed typically address this by either parallelization on GPU-hardware, or by introducing methodically novel techniques into CPU-oriented algorithms. Stochastic gradient descent (SGD) optimization and variations thereof have proven to drastically reduce the computational burden for CPU-based image registration, but have not been successfully applied in GPU hardware due to its stochastic nature. This paper proposes 1) NiftyRegSGD, a SGD optimization for the GPU-based image registration tool NiftyReg, 2) random chunk sampler, a new random sampling strategy that better utilizes the memory bandwidth of GPU hardware. Experiments have been performed on 3D lung CT data of 19 patients, which compared NiftyRegSGD (with and without random chunk sampler) with CPU-based elastix Fast Adaptive SGD (FASGD) and NiftyReg. The registration runtime was 21.5s, 4.4s and 2.8s for elastix-FASGD, NiftyRegSGD without, and NiftyRegSGD with random chunk sampling, respectively, while similar accuracy was obtained. Our method is publicly available at

Paper Details

Date Published: 2 March 2018
PDF: 7 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740R (2 March 2018); doi: 10.1117/12.2293098
Show Author Affiliations
Parag Bhosale, Technische Univ. Delft (Netherlands)
Leiden Univ. Medical Ctr. (Netherlands)
Marius Staring, Leiden Univ. Medical Ctr. (Netherlands)
Technische Univ. Delft (Netherlands)
Zaid Al-Ars, Technische Univ. Delft (Netherlands)
Floris F. Berendsen, Leiden Univ. Medical Ctr. (Netherlands)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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