Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.
Fiche publication
Date publication
octobre 2020
Journal
European journal of nuclear medicine and molecular imaging
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CASASNOVAS Olivier
Tous les auteurs :
Blanc-Durand P, Jégou S, Kanoun S, Berriolo-Riedinger A, Bodet-Milin C, Kraeber-Bodéré F, Carlier T, Le Gouill S, Casasnovas RO, Meignan M, Itti E
Lien Pubmed
Résumé
Lymphoma lesion detection and segmentation on whole-body FDG-PET/CT are a challenging task because of the diversity of involved nodes, organs or physiological uptakes. We sought to investigate the performances of a three-dimensional (3D) convolutional neural network (CNN) to automatically segment total metabolic tumour volume (TMTV) in large datasets of patients with diffuse large B cell lymphoma (DLBCL).
Mots clés
Convolutional neural network, Deep learning, Lymphoma, Positron emission tomography, Segmentation, Total metabolic tumour volume, U-net
Référence
Eur J Nucl Med Mol Imaging. 2020 Oct 24;: