HyperPredict: Estimating Hyperparameter Effects for Instance-Specific Regularization in Deformable Image Registration

ArXiv Pub Date : 2024-03-04 DOI:10.59275/j.melba.2024-d434
Aisha L. Shuaibu, Ivor J. A. Simpson
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Abstract

Methods for medical image registration infer geometric transformations that align pairs, or 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 deformable 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, which serves as a trade-off between loss terms. In some approaches and 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 novel method for evaluating the influence of hyperparameters and subsequently selecting an optimal value for given pair of images. 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 measures 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 (e.g. overlap of specific anatomical regions of interest, smoothness/plausibility of the final displacement field). We evaluate our proposed method on the OASIS brain MR standard benchmark 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.
HyperPredict:在可变形图像配准中估计超参数效应以实现特定实例正则化
医学图像配准方法通过最大限度地提高图像相似度指标,推断出使图像对或图像组配准的几何变换。由于多个解决方案可能具有相同的似然性,因此这个问题并不完美,而且单纯优化图像相似性可能会产生难以置信的可变形变换。因此,要获得有意义的配准结果,正则化条件是必不可少的。不过,这需要引入至少一个超参数(通常称为 λ),作为损失项之间的权衡。在某些方法和情况下,估计变换的质量在很大程度上取决于超参数的选择,而且可能需要根据数据的特性做出不同的选择。分析这些超参数的影响需要标注数据,而这些数据在测试时并不常见。在本文中,我们提出了一种新方法,用于评估超参数的影响,并随后为给定的图像对选择最佳值。我们将这种方法称为 HyperPredict,它采用了多层感知器(Multi-Layer Perceptron),通过预测所产生的分割重叠度和变形平滑度,学习选择特定超参数对图像进行注册的效果。这种方法使我们能够在测试时选择最佳超参数,而无需标注数据,从而无需采用一刀切的交叉验证方法。此外,用于定义最佳超参数的标准在训练后是灵活的,允许我们有效地选择特定属性(如特定解剖学感兴趣区的重叠、最终位移场的平滑度/可信度)。我们使用最新的深度学习方法(cLapIRN)和算法方法(Niftyreg)在 OASIS 脑磁共振标准基准数据集上评估了我们提出的方法。我们的结果表明,我们在预测正则化超参数的效果方面表现出色,并凸显了我们针对特定图像对的超参数选择方法的优势。
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