IEEE Trans Med Imaging. 2020 Jul;39(7):2506-2517. doi: 10.1109/TMI.2020.2972616. Epub 2020 Feb 10.
Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets. When applied to a 3D dataset the algorithm calculates the inverse of the registration vector field simultaneously. For registration we employed a U-Net combined with a coarse to fine approach and a differential spatial transformer module. The algorithm was thoroughly tested with multiple 4D and 3D datasets publicly available. The results show that the presented approach is able to track periodic motion and to yield a competitive registration accuracy. Possible applications are the use as a stand-alone algorithm for 3D and 4D motion tracking or in the beginning of studies until enough datasets for a separate training phase are available.
变形图像配准是医学成像中一个非常重要的研究领域。最近，该领域发表了多篇深度学习方法的论文，显示出了有前景的结果。然而，深度学习方法的缺点是需要大量的训练数据集，并且无法对与训练数据集不同的未见图像进行配准。单次学习不需要大量的训练数据集，并且已经被证明适用于 3D 数据。在这项工作中，我们提出了一种用于 3D 和 4D 数据集周期性运动跟踪的单次配准方法。当应用于 3D 数据集时，该算法会同时计算配准向量场的逆。我们使用了 U-Net 结合粗到精的方法和差分空间变换模块进行配准。该算法经过了多个公开的 4D 和 3D 数据集的全面测试。结果表明，所提出的方法能够跟踪周期性运动，并具有竞争力的配准精度。可能的应用是作为 3D 和 4D 运动跟踪的独立算法使用，或在有足够的数据集用于单独的训练阶段之前用于研究的开始阶段。
