One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
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DOI：
10.1109/tmi.2020.2972616
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发表时间：
2020-07-01
影响因子：
10.6
通讯作者：
Baltas, Dimos
中科院分区：
文献类型：
--
作者：
Fechter, Tobias;Baltas, Dimos
可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，在这一领域发表了多种深度学习方法，取得了令人振奋的结果。然而，深度学习方法的缺点是需要大量的训练数据集，并且无法配准与训练数据集不同的未见图像。一次学习不需要大的训练数据集，并且已经被证明适用于3D数据。在这项工作中，我们提出了一种在3D和4D数据集中进行周期性运动跟踪的单次配准方法。当应用于3D数据集时，该算法同时计算配准向量场的逆。对于配准，我们使用了U网，结合了由粗到精的方法和差分空间变换模块。该算法在公开可用的多个4D和3D数据集上进行了彻底的测试。实验结果表明，该方法能够较好地跟踪周期性运动，并具有较好的配准精度。可能的应用是用作3D和4D运动跟踪的独立算法，或者在研究开始时使用，直到有足够的数据集用于单独的训练阶段。
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.