MAD：用于图像配准的模态无关距离度量
MAD: Modality Agnostic Distance Measure for Image Registration
AI总结：
本文提出模态无关距离（MAD），利用随机卷积学习图像几何结构，在单模态数据上训练即可实现多模态图像配准，且捕获范围优于传统度量。
摘要：
多模态图像配准是许多医学应用中的关键预处理步骤。然而，由于不同成像模态之间复杂的强度关系，导致图像外观存在较大差异，这使得多模态图像配准成为一项具有挑战性的任务。多模态图像配准的成功，无论是基于传统方法还是基于学习的方法，都取决于选择合适的距离（或相似性）度量。特别是，深度学习配准算法在尝试配准来自“未见”模态的数据时，往往缺乏准确性，甚至完全失败。在这项工作中，我们提出了模态无关距离（MAD），一种深度图像距离度量，它利用随机卷积来学习图像的固有几何结构，同时对外观的大幅变化具有鲁棒性。随机卷积是保持几何结构的模块，我们用它来模拟无限数量的合成模态，从而在训练期间无需对齐的配对数据。因此，我们可以在单模态数据集上训练MAD，并成功将其应用于多模态数据集。我们证明，MAD不仅能成功地进行多模态图像的仿射配准，而且其捕获范围比互信息和归一化梯度场等传统度量更大。
英文摘要：
Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between different imaging modalities, which can result in large discrepancy in image appearance. The success of multi-modal image registration, whether it is conventional or learning based, is predicated upon the choice of an appropriate distance (or similarity) measure. Particularly, deep learning registration algorithms lack in accuracy or even fail completely when attempting to register data from an "unseen" modality. In this work, we present Modality Agnostic Distance (MAD), a deep image distance}] measure that utilises random convolutions to learn the inherent geometry of the images while being robust to large appearance changes. Random convolutions are geometry-preserving modules which we use to simulate an infinite number of synthetic modalities alleviating the need for aligned paired data during training. We can therefore train MAD on a mono-modal dataset and successfully apply it to a multi-modal dataset. We demonstrate that not only can MAD affinely register multi-modal images successfully, but it has also a larger capture range than traditional measures such as Mutual Information and Normalised Gradient Fields.
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