Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration
This addresses a critical gap in medical imaging by enabling efficient and cost-effective registration of pathological images, which is incremental as it builds on existing unsupervised techniques but specifically targets focal tissue challenges.
The paper tackles the problem of pathological image registration, which is often hindered by focal tissue issues like loss of spatial correspondence and abnormal distortion, by proposing GIRNet, an unsupervised method that integrates segmentation and inpainting; experimental results on MRI datasets show it accurately registers images and identifies lesions.
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.