The registration of pathological images plays an important role in medical applications. Despite its significance, most researchers in this field primarily focus on the registration of normal tissue into normal tissue. The negative impact of focal tissue, such as the loss of spatial correspondence information and the abnormal distortion of tissue, are rarely considered. In this paper, we propose GIRNet, a novel unsupervised approach for pathological image registration by incorporating segmentation and inpainting through the principles of Generation, Inpainting, and Registration (GIR). The registration, segmentation, and inpainting modules are trained simultaneously in a co-learning manner so that the segmentation of the focal area and the registration of inpainted pairs can improve collaboratively. Overall, the registration of pathological images is achieved in a completely unsupervised learning framework. Experimental results on multiple datasets, including Magnetic Resonance Imaging (MRI) of T1 sequences, demonstrate the efficacy of our proposed method. Our results show that our method can accurately achieve the registration of pathological images and identify lesions even in challenging imaging modalities. Our unsupervised approach offers a promising solution for the efficient and cost-effective registration of pathological images. Our code is available at https://github.com/brain-intelligence-lab/GIRNet.

翻译：病理图像的配准在医学应用中具有重要作用。尽管意义重大，但该领域多数研究主要关注正常组织之间的配准。局灶组织带来的负面影响，如空间对应信息的丢失和组织的异常形变，极少被考虑。本文提出GIRNet——一种通过融合生成、修复与配准（GIR）原理，将分割与修复技术相结合的病理图像无监督配准新方法。配准、分割与修复模块以协同学习方式同步训练，使得病灶区域的分割与修复图像对的配准能够相互促进优化。总体而言，病理图像的配准是在完全无监督的学习框架下实现的。在包含T1序列磁共振成像（MRI）在内的多个数据集上的实验结果表明了所提方法的有效性。结果显示，该方法能够精确实现病理图像配准，即使在具有挑战性的成像模态中也能识别病灶。我们的无监督方法为高效且经济的病理图像配准提供了有前景的解决方案。代码已开源在https://github.com/brain-intelligence-lab/GIRNet。