Taking a multi-objective optimization approach to deformable image registration has recently gained attention, because
such an approach removes the requirement of manually tuning the weights of all the involved objectives. Especially for
problems that require large complex deformations, this is a non-trivial task. From the resulting Pareto set of solutions one
can then much more insightfully select a registration outcome that is most suitable for the problem at hand. To serve as
an internal optimization engine, currently used multi-objective algorithms are competent, but rather inefficient. In this
paper we largely improve upon this by introducing a multi-objective real-valued adaptation of the recently introduced
Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) for discrete optimization. In this work, GOMEA is
tailored specifically to the problem of deformable image registration to obtain substantially improved efficiency. This
improvement is achieved by exploiting a key strength of GOMEA: iteratively improving small parts of solutions,
allowing to faster exploit the impact of such updates on the objectives at hand through partial evaluations. We performed
experiments on three registration problems. In particular, an artificial problem containing a disappearing structure, a pair
of pre- and post-operative breast CT scans, and a pair of breast MRI scans acquired in prone and supine position were
considered. Results show that compared to the previously used evolutionary algorithm, GOMEA obtains a speed-up of
up to a factor of ~1600 on the tested registration problems while achieving registration outcomes of similar quality.
Anton Bouter, Tanja Alderliesten, and Peter A. N. Bosman, "A novel model-based evolutionary algorithm for multi-objective deformable image registration with content mismatch and large deformations: benchmarking efficiency and quality," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013312 (Presented at SPIE Medical Imaging: February 13, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254144.
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