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

Tumor segmentation in PET-CT images is challenging due to the dual nature ofthe acquired information: low metabolic information in CT and low spatialresolution in PET. U-Net architecture is the most common and widely recognizedapproach when developing a fully automatic image segmentation method in themedical field. We proposed a two-step approach, aiming to refine and improvethe segmentation performances of tumoral lesions in PET-CT. The first stepgenerates a prior tumor appearance map from the PET-CT volumes, regarded asprior tumor information. The second step, consisting of a standard U-Net,receives the prior tumor appearance map and PET-CT images to generate thelesion mask. We evaluated the method on the 1014 cases available for theAutoPET 2022 challenge, and the results showed an average Dice score of 0.701on the positive cases.

PriorNet: lesion segmentation in PET-CT including prior tumor appearance information