Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3510-3513. doi: 10.1109/EMBC48229.2022.9871220.
Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based registration methods have demonstrated superior per-formance compared to iterative methods in just a fraction of the time. Most of the learning-based methods have focused on mono-modal image registration. The extension to multi-modal registration depends on the use of an appropriate similarity function, such as the mutual information (MI). We propose guiding the training of a deep learning-based registration method with MI estimation between an image-pair in an end-to-end trainable network. Our results show that a small, 2-layer network produces competitive results in both mono- and multi-modal registration, with sub-second run-times. Comparisons to both iterative and deep learning-based methods show that our MI-based method produces topologically and qualitatively superior results with an extremely low rate of non-diffeomorphic transformations. Real-time clinical application will benefit from a better visual matching of anatomical structures and less registration failures/outliers.
许多在图像引导手术和治疗中的应用都需要快速且可靠的非线性、多模态图像配准。最近提出的基于无监督深度学习的配准方法在时间上只需迭代方法的一小部分就能展示出优越的性能。大多数基于学习的方法都集中在单模态图像配准上。多模态配准的扩展取决于使用适当的相似性函数，例如互信息（MI）。我们提出通过在端到端可训练网络中估计图像对之间的 MI 来指导基于深度学习的配准方法的训练。我们的结果表明，一个小的 2 层网络在单模态和多模态配准中都能产生具有竞争力的结果，运行时间在秒级以下。与迭代和基于深度学习的方法的比较表明，我们的基于 MI 的方法能够产生拓扑和定性上优越的结果，同时具有极低的非刚性变换率。实时临床应用将受益于更好的解剖结构视觉匹配和更少的配准失败/异常值。
