Deformable image registration (DIR) involves optimization of multiple
conflicting objectives, however, not many existing DIR algorithms are
multi-objective (MO). Further, while there has been progress in the design of
deep learning algorithms for DIR, there is no work in the direction of MO DIR
using deep learning. In this paper, we fill this gap by combining a recently
proposed approach for MO training of neural networks with a well-known deep
neural network for DIR and create a deep learning based MO DIR approach. We
evaluate the proposed approach for DIR of pelvic magnetic resonance imaging
(MRI) scans. We experimentally demonstrate that the proposed MO DIR approach --
providing multiple registration outputs for each patient that each correspond
to a different trade-off between the objectives -- has additional desirable
properties from a clinical use point-of-view as compared to providing a single
DIR output. The experiments also show that the proposed MO DIR approach
provides a better spread of DIR outputs across the entire trade-off front than
simply training multiple neural networks with weights for each objective
sampled from a grid of possible values.

通过将最近提出的多目标神经网络训练方法与深度神经网络相结合，本文填补了多目标深度学习变形图像配准的研究空白，并通过对盆腔磁共振成像扫描的配准实验，实验表明多目标深度学习变形图像配准方法相比于提供单一配准结果，在临床使用角度具有更多的优点，能够提供多个在不同目标权衡条件下的注册结果。实验还表明，相比于使用多个神经网络进行训练，从可能取值网格中抽样每个目标的权重，所提出的多目标深度学习变形图像配准方法能够在整个权衡前沿获得更好分布的配准结果。