面向病理图像配准的协同学习语义感知无监督分割
Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration
AI总结：
针对病理局灶组织破坏空间对应的问题，提出 GIRNet，以协同学习联合训练分割、修复与配准模块，在完全无监督框架下提升多模态病理图像配准和病灶识别效果。
摘要：
病理图像配准在医学应用中发挥着重要作用。尽管其意义重大，该领域的大多数研究者主要关注正常组织到正常组织的配准，很少考虑局灶组织的负面影响，例如空间对应信息丢失和组织异常扭曲。本文提出 GIRNet，这是一种新颖的病理图像配准无监督方法，通过生成、修复和配准（GIR）原则融合分割与修复。配准、分割和修复模块以协同学习方式同时训练，使局灶区域分割与修复图像对配准能够相互促进。总体而言，病理图像配准在完全无监督学习框架内实现。在包括 T1 序列磁共振成像（MRI）在内的多个数据集上的实验结果证明了所提方法的有效性。结果表明，该方法即使在具有挑战性的成像模态中，也能准确完成病理图像配准并识别病灶。这种无监督方法为高效、低成本的病理图像配准提供了有前景的解决方案。代码可在 https://github.com/brain-intelligence-lab/GIRNet 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
