IEEE Trans Med Imaging. 2020 May;39(5):1594-1604. doi: 10.1109/TMI.2019.2953788. Epub 2019 Nov 15.
Deep learning-based methods for deformable image registration are attractive alternatives to conventional registration methods because of their short registration times. However, these methods often fail to estimate larger displacements in complex deformation fields, for which a multi-resolution strategy is required. In this article, we propose to train neural networks progressively to address this problem. Instead of training a large convolutional neural network on the registration task all at once, we initially train smaller versions of the network on lower resolution versions of the images and deformation fields. During training, we progressively expand the network with additional layers that are trained on higher resolution data. We show that this way of training allows a network to learn larger displacements without sacrificing registration accuracy and that the resulting network is less sensitive to large misregistrations compared to training the full network all at once. We generate a large number of ground truth example data by applying random synthetic transformations to a training set of images, and test the network on the problem of intrapatient lung CT registration. We analyze the learned representations in the progressively growing network to assess how the progressive learning strategy influences training. Finally, we show that a progressive training procedure leads to improved registration accuracy when learning large and complex deformations.
基于深度学习的可变形图像配准方法因其配准时间短而成为传统配准方法的有吸引力的替代方法。然而，这些方法往往无法估计复杂变形场中的较大位移，这需要采用多分辨率策略。在本文中，我们提出了一种渐进式训练神经网络的方法来解决这个问题。我们不是一次性地在配准任务上训练一个大型卷积神经网络，而是首先在图像和变形场的较低分辨率版本上训练较小版本的网络。在训练过程中，我们使用在更高分辨率数据上训练的附加层逐步扩展网络。我们表明，这种训练方式允许网络在不牺牲配准精度的情况下学习更大的位移，并且与一次性训练整个网络相比，所得到的网络对大的配准错误不那么敏感。我们通过对一组图像的训练集应用随机合成变换生成大量真实示例数据，并在单患者肺部 CT 配准问题上对网络进行测试。我们分析了逐渐增长的网络中的学习表示，以评估渐进式学习策略如何影响训练。最后，我们表明，渐进式训练过程在学习大而复杂的变形时可以提高配准精度。
