Authors:
Katharina Krämer
1
;
Stefan Müller
1
and
Michael Kosterhon
2
Affiliations:
1
Institute of Computervisualistics, University of Koblenz, Universitätsstraße 1, 56070 Koblenz, Germany
;
2
Medical Center Johannes Gutenberg University, Langenbeckstraße 1, 55131 Mainz, Germany
Keyword(s):
Spondylodesis Surgery, Medical Image Registration, Particle Swarm Optimization, Deep Learning, Parameter Optimization, Benchmark Functions, Search Space Volume.
Abstract:
A novel parameter training approach for Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) using Deep Learning is proposed. In PSO, balancing exploration and exploitation is crucial, with inertia governing parameter space sampling. This work presents a method for training transfer function parameters that adjust the inertia weight based on a particle’s individual search ability (ISA) in each dimension. A neural network is used to train the parameters of this transfer function, which then maps the ISA value to a new inertia weight. During inference, the best possible success ratio and lowest average error are used as network inputs to predict optimal parameters. Interestingly, the parameters across different objective functions are similar and assume values that may appear spatially implausible, yet outperform all other considered value expressions. We evaluate the proposed method Deep Learning-Tuned Adaptive Inertia Weight (TAIW) against three inertia strategies: Constant
Inertia Strategy (CIS), Linear Decreasing Inertia (LDI), Adaptive Inertia Weight (AIW) on three benchmark functions. Additionally, we apply these PSO inertia strategies to medical image registration, utilizing digitally reconstructed radiographs (DRRs). The results show promising improvements in alignment accuracy using TAIW. Finally, we introduce a metric that assesses search effectiveness based on multidimensional search space volumes.
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