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.
可变形的图像配准是医学图像中非常重要的研究领域。最近该领域发表的很多深度学习方法展示了显著的效果。然而，需要大量的训练数据以及无法配准在训练数据集中从未见过的的图像仍然是深度学习的缺陷。One shot学习不需要大规模的训练数据集并且已经被正式可应用于3D数据集。在这份工作中，我们展示了一个one shot配准方法用于跟踪3D和4D数据集的周期性运动。当应用于3D数据集，该算法同时计算配准向量场的逆运算。对于配准，我们应用结合从粗到细的U-Net方法以及一个差分空间转换模型。算法完全在多个4D和3D公开数据集上进行测试。结果表明提出的方法能够跟踪周期运动并且实现可竞争的配准精确度。可单独应用于3D和4D运动跟踪或者在研究的初始阶段直到有足够的数据集可用于单独的训练。