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

Parag Bhosale, Marius Staring, Zaid Al-Ars and Floris F. Berendsen

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.

 

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Copyright © 2018 by the authors. Published version © 2018 by SPIE. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@inproceedings{Bhosale:2018,
author = "{Parag Bhosale and Marius Staring and Zaid Al-Ars and Floris F. Berendsen}",
title = "{GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications}",
booktitle = "{SPIE Medical Imaging: Image Processing}",
editor = "{Elsa A. Angelini and Bennett A. Landman}",
address = "{Houston, Texas, USA}",
series = "{Proceedings of SPIE}",
month = "{February}",
year = "{2018}",
}

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