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

Template generation is a crucial step of groupwise image registration whichdeforms a group of subjects into a common space. Existing traditional and deeplearning-based methods can generate high-quality template images. However, theysuffer from substantial time costs or limited application scenarios like fixedgroup size. In this paper, we propose an efficient groupwise templategenerative framework based on variational autoencoder models utilizing thearithmetic property of latent representation of input images. We acquire thelatent vectors of each input and use the average vector to construct thetemplate through the decoder. Therefore, the method can be applied to groups ofany scale. Secondly, we explore a siamese training scheme that feeds two imagesto the shared-weight twin networks and compares the distances between inputsand the generated template to prompt the template to be close to the implicitcenter. We conduct experiments on 3D brain MRI scans of groups of differentsizes. Results show that our framework can achieve comparable and even betterperformance to baselines, with runtime decreased to seconds.

SETGen: Scalable and Efficient Template Generation Framework for Groupwise Medical Image Registration