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

Magnetic Resonance Imaging (MRI) is widely used in the pathological andfunctional studies of the brain, such as epilepsy, tumor diagnosis, etc.Automated accurate brain tissue segmentation like cerebro-spinal fluid (CSF),gray matter (GM), white matter (WM) is the basis of these studies and manyresearchers are seeking it to the best. Based on the truth that multi-channelsegmentation network with its own ground truth achieves up to average diceratio 0.98, we propose a novel method that we add a fourth channel with theground truth of the most similar image's obtained by CBIR from the database.The results show that the method improves the segmentation performance, asmeasured by average dice ratio, by approximately 0.01 in the MRBrainS18database. In addition, our method is concise and robust, which can be used toany network architecture that needs not be modified a lot.

A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation