Deformable image registration is able to achieve fast and accurate alignment between a pair of images and thus plays an important role in many medical image studies. The current deep learning (DL)-based image registration approaches directly learn the spatial transformation from one image to another by leveraging a convolutional neural network, requiring ground truth or similarity metric. Nevertheless, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within images. Moreover, DL-based methods often estimate global spatial transformations of image directly, which never pays attention to region spatial transformations of ROIs within images. In this paper, we present a novel dual-flow transformation network with region consistency constraint which maximizes the similarity of ROIs within a pair of images and estimates both global and region spatial transformations simultaneously. Experiments on four public 3D MRI datasets show that the proposed method achieves the best registration performance in accuracy and generalization compared with other state-of-the-art methods.
翻译:然而,这些方法只使用全球相似的能量功能来评价一副图像的相似性,而这两张图像忽视了图像中感兴趣的区域(ROIs)的相似性。此外,基于DL的方法往往直接估计图像的全球空间变异,从不注意图像中的ROI的区域空间变异。在本文中,我们展示了一个具有区域一致性制约的新型双流变换网络,这种变换网络使一对图像中的ROI的相似性最大化,同时估计全球和区域空间变异。对四张公开的 3D MRI 数据集的实验表明,与其它最先进的方法相比,拟议的方法在准确性和普遍性方面实现了最佳的登记表现。