GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications

Conference Paper (2018)
Author(s)

Parag Bhosale (Leiden University Medical Center)

M. Staring (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)

Z Al-Ars (TU Delft - Computer Engineering)

Floris Berendsen (Leiden University Medical Center)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2018 P.S. Bhosale, M. Staring, Z. Al-Ars, Floris F. Berendsen
DOI related publication
https://doi.org/10.1117/12.2293098
More Info
expand_more
Publication Year
2018
Language
English
Copyright
© 2018 P.S. Bhosale, M. Staring, Z. Al-Ars, Floris F. Berendsen
Research Group
Pattern Recognition and Bioinformatics
Pages (from-to)
1-8
ISBN (electronic)
9781510616370
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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 https://github.com/SuperElastix/NiftyRegSGD.

Files

45239393_105740R.pdf
(pdf | 0.627 Mb)
License info not available