Abstract
When choosing a deformable image registration (DIR) approach for images with large deformations and content mismatch, the realism of found transformations often needs to be traded off against the required runtime. DIR approaches using deep learning (DL) techniques have shown remarkable promise in instantly predicting a transformation. However, on difficult registration problems, the realism of these transformations can fall short. DIR approaches using biomechanical, finite element modeling (FEM) techniques can find more realistic transformations, but tend to require much longer runtimes. This work proposes the first hybrid approach to combine them, with the aim of getting the best of both worlds. This hybrid approach, called DL-MOREA, combines a recently introduced multi-objective DL-based DIR approach which leverages the VoxelMorph framework, called DL-MODIR, with MOREA, an evolutionary algorithm-based, multi-objective DIR approach in which a FEM-like biomechanical mesh transformation model is used. In our proposed hybrid approach, the DL results are used to smartly initialize MOREA, with the aim of more efficiently optimizing its mesh transformation model. We empirically compare DL-MOREA against its components, DL-MODIR and MOREA, on CT scan pairs capturing large bladder filling differences of 15 cervical cancer patients. While MOREA requires a median runtime of 45 minutes, DL-MOREA can already find high-quality transformations after 5 minutes. Compared to the DL-MODIR transformations, the transformations found by DL-MOREA exhibit far less folding and improve or preserve the bladder contour distance error.
Abstract (translated)
在选择用于处理图像变形大且内容不匹配问题的可变形图像配准（DIR）方法时，通常需要权衡变换的真实性和所需运行时间。采用深度学习（DL）技术的DIR方法已显示出能够瞬间预测变换的显著潜力。然而，在困难的注册问题上，这些变换的真实度可能不足。使用生物力学、有限元建模（FEM）技术的DIR方法可以找到更真实的变换，但往往需要更长的运行时间。这项工作提出了首个结合两者的混合方法，旨在获得最佳效果。这种混合方法被命名为DL-MOREA，它将最近引入的一种基于多目标深度学习的DIR方法DL-MODIR（采用VoxelMorph框架）与MOREA相结合，后者是一种基于进化算法的多目标DIR方法，使用类似FEM的生物力学网格变换模型。 在我们提出的混合方法中，DL的结果被用于智能初始化MOREA，旨在更高效地优化其网格变换模型。我们在15名宫颈癌患者的CT扫描对（这些对捕捉了大范围膀胱充盈差异）上将DL-MOREA与其组件DL-MODIR和MOREA进行了实证对比。 与MOREA需要中位运行时间45分钟相比，DL-MOREA能够在短短5分钟后找到高质量的变换。与DL-MODIR发现的变换相比，DL-MOREA所发现的变换表现出更少的折叠现象，并且改善或保持了膀胱轮廓距离误差。
URL
https://arxiv.org/abs/2501.16525
