  PET and CT are two modalities widely used in medical image analysis.
Accurately detecting and segmenting lymphomas from these two imaging modalities
are critical tasks for cancer staging and radiotherapy planning. However, this
task is still challenging due to the complexity of PET/CT images, and the
computation cost to process 3D data. In this paper, a segmentation method based
on belief functions is proposed to segment lymphomas in 3D PET/CT images. The
architecture is composed of a feature extraction module and an evidential
segmentation (ES) module. The ES module outputs not only segmentation results
(binary maps indicating the presence or absence of lymphoma in each voxel) but
also uncertainty maps quantifying the classification uncertainty. The whole
model is optimized by minimizing Dice and uncertainty loss functions to
increase segmentation accuracy. The method was evaluated on a database of 173
patients with diffuse large b-cell lymphoma. Quantitative and qualitative
results show that our method outperforms the state-of-the-art methods.
