Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images.
Fiche publication
Date publication
janvier 2024
Journal
Frontiers in oncology
Auteurs
Membres identifiés du Cancéropôle Est :
Dr BESSIERES Igor
Tous les auteurs :
Zhou Y, Lalande A, Chevalier C, Baude J, Aubignac L, Boudet J, Bessieres I
Lien Pubmed
Résumé
Linear accelerator (linac) incorporating a magnetic resonance (MR) imaging device providing enhanced soft tissue contrast is particularly suited for abdominal radiation therapy. In particular, accurate segmentation for abdominal tumors and organs at risk (OARs) required for the treatment planning is becoming possible. Currently, this segmentation is performed manually by radiation oncologists. This process is very time consuming and subject to inter and intra operator variabilities. In this work, deep learning based automatic segmentation solutions were investigated for abdominal OARs on 0.35 T MR-images.
Mots clés
MR images, MR-Linac, automatic segmentation, deep learning, nnUNet
Référence
Front Oncol. 2024 01 8;13:1285924