English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The registration of pathological images plays an important role in medicalapplications. Despite its significance, most researchers in this fieldprimarily focus on the registration of normal tissue into normal tissue. Thenegative impact of focal tissue, such as the loss of spatial correspondenceinformation and the abnormal distortion of tissue, are rarely considered. Inthis paper, we propose GIRNet, a novel unsupervised approach for pathologicalimage registration by incorporating segmentation and inpainting through theprinciples of Generation, Inpainting, and Registration (GIR). The registration,segmentation, and inpainting modules are trained simultaneously in aco-learning manner so that the segmentation of the focal area and theregistration of inpainted pairs can improve collaboratively. Overall, theregistration of pathological images is achieved in a completely unsupervisedlearning framework. Experimental results on multiple datasets, includingMagnetic Resonance Imaging (MRI) of T1 sequences, demonstrate the efficacy ofour proposed method. Our results show that our method can accurately achievethe registration of pathological images and identify lesions even inchallenging imaging modalities. Our unsupervised approach offers a promisingsolution for the efficient and cost-effective registration of pathologicalimages. Our code is available athttps://github.com/brain-intelligence-lab/GIRNet.

Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration