  Lymphoma detection and segmentation from whole-body Positron Emission
Tomography/Computed Tomography (PET/CT) volumes are crucial for surgical
indication and radiotherapy. Designing automatic segmentation methods capable
of effectively exploiting the information from PET and CT as well as resolving
their uncertainty remain a challenge. In this paper, we propose an lymphoma
segmentation model using an UNet with an evidential PET/CT fusion layer.
Single-modality volumes are trained separately to get initial segmentation maps
and an evidential fusion layer is proposed to fuse the two pieces of evidence
using Dempster-Shafer theory (DST). Moreover, a multi-task loss function is
proposed: 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 added
to constrain the final segmentation. We evaluate our proposal on a database of
polycentric PET/CT volumes of patients treated for lymphoma, delineated by the
experts. Our method get accurate segmentation results with Dice score of 0.726,
without any user interaction. Quantitative results show that our method is
superior to the state-of-the-art methods.
