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

Lymphoma detection and segmentation from whole-body Positron EmissionTomography/Computed Tomography (PET/CT) volumes are crucial for surgicalindication and radiotherapy. Designing automatic segmentation methods capableof effectively exploiting the information from PET and CT as well as resolvingtheir uncertainty remain a challenge. In this paper, we propose an lymphomasegmentation model using an UNet with an evidential PET/CT fusion layer.Single-modality volumes are trained separately to get initial segmentation mapsand an evidential fusion layer is proposed to fuse the two pieces of evidenceusing Dempster-Shafer theory (DST). Moreover, a multi-task loss function isproposed: in addition to the use of the Dice loss for PET and CT segmentation,a loss function based on the concordance between the two segmentation is addedto constrain the final segmentation. We evaluate our proposal on a database ofpolycentric PET/CT volumes of patients treated for lymphoma, delineated by theexperts. Our method get accurate segmentation results with Dice score of 0.726,without any user interaction. Quantitative results show that our method issuperior to the state-of-the-art methods.

Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation