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.

翻译：在需要大变形的情况下，找到一种将一幅图像变换为另一幅图像的符合实际的形变，被视为医学图像分析中的关键挑战。若能有恰当的图像配准方法实现这一目标，将催生众多需要图像间信息传递的应用。目前，许多现有方法因每次使用前需要大量配置工作，或无法（实际地）捕捉大变形，阻碍了临床推广。近期一种结合多目标实值基因池最优混合进化算法（MO-RV-GOMEA）与双动态网格变形模型的多目标方法显示出潜力，揭示了图像配准问题中固有的权衡关系，并在二维领域实现了大变形建模。本研究在此潜力基础上提出MOREA：首个基于进化算法、能够处理大变形的三维图像可变形配准多目标方法。MOREA包含一个用于物理合理性的三维生物力学网格模型，并实现了完全GPU加速。我们将MOREA与两种最新方法在4名宫颈癌患者的腹部CT扫描数据上进行对比，后两种方法针对每位患者进行了最优配置。在无需按患者进行配置的情况下，MOREA在代表最困难病例的4例患者中的3例上显著优于这些方法。