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

Time is a fundamental factor during stroke treatments. A fast, automaticapproach that segments the ischemic regions helps treatment decisions. Inclinical use today, a set of color-coded parametric maps generated fromcomputed tomography perfusion (CTP) images are investigated manually to decidea treatment plan. We propose an automatic method based on a neural networkusing a set of parametric maps to segment the two ischemic regions (core andpenumbra) in patients affected by acute ischemic stroke. Our model is based ona convolution-deconvolution bottleneck structure with multi-input and slowfusion. A loss function based on the focal Tversky index addresses the dataimbalance issue. The proposed architecture demonstrates effective performanceand results comparable to the ground truth annotated by neuroradiologists. ADice coefficient of 0.81 for penumbra and 0.52 for core over the large vesselocclusion test set is achieved. The full implementation is available at:https://git.io/JtFGb.

Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection