  Finding a realistic deformation that transforms one image into another, in
case large deformations are required, is considered a key challenge in medical
image analysis. Having a proper image registration approach to achieve this
could unleash a number of applications requiring information to be transferred
between images. Clinical adoption is currently hampered by many existing
methods requiring extensive configuration effort before each use, or not being
able to (realistically) capture large deformations. A recent multi-objective
approach that uses the Multi-Objective Real-Valued Gene-pool Optimal Mixing
Evolutionary Algorithm (MO-RV-GOMEA) and a dual-dynamic mesh transformation
model has shown promise, exposing the trade-offs inherent to image registration
problems and modeling large deformations in 2D. This work builds on this
promise and introduces MOREA: the first evolutionary algorithm-based
multi-objective approach to deformable registration of 3D images capable of
tackling large deformations. MOREA includes a 3D biomechanical mesh model for
physical plausibility and is fully GPU-accelerated. We compare MOREA to two
state-of-the-art approaches on abdominal CT scans of 4 cervical cancer
patients, with the latter two approaches configured for the best results per
patient. Without requiring per-patient configuration, MOREA significantly
outperforms these approaches on 3 of the 4 patients that represent the most
difficult cases.
