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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Image registration is one of the most challenging problems in medical imageanalysis. In the recent years, deep learning based approaches became quitepopular, providing fast and performing registration strategies. In this shortpaper, we summarise our work presented on Learn2Reg challenge 2020. The maincontributions of our work rely on (i) a symmetric formulation, predicting thetransformations from source to target and from target to source simultaneously,enforcing the trained representations to be similar and (ii) integration ofvariety of publicly available datasets used both for pretraining and foraugmenting segmentation labels. Our method reports a mean dice of $0.64$ fortask 3 and $0.85$ for task 4 on the test sets, taking third place on thechallenge. Our code and models are publicly available athttps://github.com/TheoEst/abdominal_registration and\https://github.com/TheoEst/hippocampus_registration.

Deep learning based registration using spatial gradients and noisy segmentation labels