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

Medical image segmentation based on deep learning often fails when deployedon images from a different domain. The domain adaptation methods aim to solvedomain-shift challenges, but still face some problems. The transfer learningmethods require annotation on the target domain, and the generativeunsupervised domain adaptation (UDA) models ignore domain-specificrepresentations, whose generated quality highly restricts segmentationperformance. In this study, we propose a novel Structure-Modal Constrained(SMC) UDA framework based on a discriminative paradigm and introduce edgestructure as a bridge between domains. The proposed multi-modal learningbackbone distills structure information from image texture to distinguishdomain-invariant edge structure. With the structure-constrained self-learningand progressive ROI, our methods segment the kidney by locating the 3D spatialstructure of the edge. We evaluated SMC-UDA on public renal segmentationdatasets, adapting from the labeled source domain (CT) to the unlabeled targetdomain (CT/MRI). The experiments show that our proposed SMC-UDA has a stronggeneralization and outperforms generative UDA methods.

SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation