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Conference Papers Year : 2021

Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation


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.
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Dates and versions

hal-03511317 , version 1 (04-01-2022)



Ling Huang, Thierry Denœux, David Tonnelet, Pierre Decazes, Su Ruan. Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. International Workshop on Machine Learning in Medical Imaging (MLMI 2021), Sep 2021, Strasbourg, France. pp.30-39, ⟨10.1007/978-3-030-87589-3_4⟩. ⟨hal-03511317⟩
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