Methods for medical image registration infer geometric transformations that
align pairs/groups of images by maximising an image similarity metric. This
problem is ill-posed as several solutions may have equivalent likelihoods, also
optimising purely for image similarity can yield implausible transformations.
For these reasons regularization terms are essential to obtain meaningful
registration results. However, this requires the introduction of at least one
hyperparameter often termed {\lambda}, that serves as a tradeoff between loss
terms. In some situations, the quality of the estimated transformation greatly
depends on hyperparameter choice, and different choices may be required
depending on the characteristics of the data. Analyzing the effect of these
hyperparameters requires labelled data, which is not commonly available at
test-time. In this paper, we propose a method for evaluating the influence of
hyperparameters and subsequently selecting an optimal value for given image
pairs. Our approach which we call HyperPredict, implements a Multi-Layer
Perceptron that learns the effect of selecting particular hyperparameters for
registering an image pair by predicting the resulting segmentation overlap and
measure of deformation smoothness. This approach enables us to select optimal
hyperparameters at test time without requiring labelled data, removing the need
for a one-size-fits-all cross-validation approach. Furthermore, the criteria
used to define optimal hyperparameter is flexible post-training, allowing us to
efficiently choose specific properties. We evaluate our proposed method on the
OASIS brain MR dataset using a recent deep learning approach(cLapIRN) and an
algorithmic method(Niftyreg). Our results demonstrate good performance in
predicting the effects of regularization hyperparameters and highlight the
benefits of our image-pair specific approach to hyperparameter selection.

该研究提出了一种名为 HyperPredict 的方法，它使用多层感知器在没有标记数据的情况下评估超参数的影响并选择最佳值，以选择特定图像对的最佳超参数进行注册，该方法在脑部医学影像数据集上表现出良好性能。