Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important brain health biomarker, from magnetic resonance (MR) images. However, most of these models only provide a global age prediction, and rely on techniques, such as saliency maps to interpret their results. These saliency maps highlight regions in the input image that were significant for the model's predictions, but they are hard to be interpreted, and saliency map values are not directly comparable across different samples. In this work, we reframe the age prediction problem from MR images to an image-to-image regression problem where we estimate the brain age for each brain voxel in MR images. We compare voxel-wise age prediction models against global age prediction models and their corresponding saliency maps. The results indicate that voxel-wise age prediction models are more interpretable, since they provide spatial information about the brain aging process, and they benefit from being quantitative.

翻译：深度学习模型在从磁共振（MR）图像中估计脑龄方面取得了最先进的结果，而脑龄是一种重要的脑健康生物标志物。然而，这些模型大多只能提供全局年龄预测，并依赖诸如显著性图等技术来解释其结果。这些显著性图可突出显示输入图像中对模型预测有显著影响的区域，但难以解释，且不同样本间的显著性图数值无法直接比较。在本研究中，我们将基于MR图像的年龄预测问题重新构建为图像到图像回归问题，从而估计MR图像中每个脑体素的脑龄。我们将体素级年龄预测模型与全局年龄预测模型及其对应的显著性图进行了比较。结果表明，体素级年龄预测模型更易解释，因为它提供了关于脑老化过程的空间信息，并且具有定量化的优势。