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

We propose a model for the joint segmentation of the liver and liver lesionsin computed tomography (CT) volumes. We build the model from two fullyconvolutional networks, connected in tandem and trained together end-to-end. Weevaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge,attaining competitive liver and liver lesion detection and segmentation scoresacross a wide range of metrics. Unlike other top performing methods, our modeloutput post-processing is trivial, we do not use data external to thechallenge, and we propose a simple single-stage model that is trainedend-to-end. However, our method nearly matches the top lesion segmentationperformance and achieves the second highest precision for lesion detectionwhile maintaining high recall.

Liver lesion segmentation informed by joint liver segmentation