  An automatic evidential segmentation method based on Dempster-Shafer theory
and deep learning is proposed to segment lymphomas from three-dimensional
Positron Emission Tomography (PET) and Computed Tomography (CT) images. The
architecture is composed of a deep feature-extraction module and an evidential
layer. The feature extraction module uses an encoder-decoder framework to
extract semantic feature vectors from 3D inputs. The evidential layer then uses
prototypes in the feature space to compute a belief function at each voxel
quantifying the uncertainty about the presence or absence of a lymphoma at this
location. Two evidential layers are compared, based on different ways of using
distances to prototypes for computing mass functions. The whole model is
trained end-to-end by minimizing the Dice loss function. The proposed
combination of deep feature extraction and evidential segmentation is shown to
outperform the baseline UNet model as well as three other state-of-the-art
models on a dataset of 173 patients.
