HyperPredict: Estimating Hyperparameter Effects for Instance-Specific Regularization in Deformable Image Registration
This addresses the need for efficient, data-specific hyperparameter tuning in medical image registration, offering a flexible alternative to cross-validation, though it is incremental as it builds on existing registration methods.
The paper tackles the problem of selecting optimal regularization hyperparameters in medical image registration, which is crucial for obtaining plausible transformations but typically requires labeled data for analysis. The proposed HyperPredict method uses a Multi-Layer Perceptron to predict segmentation overlap and deformation smoothness, enabling instance-specific hyperparameter selection without labeled data at test time, and demonstrates good performance on the OASIS brain MR dataset with cLapIRN and Niftyreg methods.
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 $λ$, 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.