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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Statistical shape models (SSMs) are an established way to represent theanatomy of a population with various clinically relevant applications. However,they typically require domain expertise, and labor-intensive landmarkannotations to construct. We address these shortcomings by proposing anunsupervised method that leverages deep geometric features and functionalcorrespondences to simultaneously learn local and global shape structuresacross population anatomies. Our pipeline significantly improves unsupervisedcorrespondence estimation for SSMs compared to baseline methods, even on highlyirregular surface topologies. We demonstrate this for two different anatomicalstructures: the thyroid and a multi-chamber heart dataset. Furthermore, ourmethod is robust enough to learn from noisy neural network predictions,potentially enabling scaling SSMs to larger patient populations without manualsegmentation annotation.

S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences