en
Scientific article
English

Fully Automated Gross Tumor Volume Delineation From PET in Head and Neck Cancer Using Deep Learning Algorithms

Published inClinical Nuclear Medicine, vol. 46, no. 11, p. 872-883
Publication date2021
Abstract

The availability of automated, accurate, and robust gross tumor volume (GTV) segmentation algorithms is critical for the management of head and neck cancer (HNC) patients. In this work, we evaluated 3 state-of-the-art deep learning algorithms combined with 8 different loss functions for PET image segmentation using a comprehensive training set and evaluated its performance on an external validation set of HNC patients.

Citation (ISO format)
SHIRI LORD, Isaac et al. Fully Automated Gross Tumor Volume Delineation From PET in Head and Neck Cancer Using Deep Learning Algorithms. In: Clinical Nuclear Medicine, 2021, vol. 46, n° 11, p. 872–883. doi: 10.1097/RLU.0000000000003789
Main files (1)
Article (Published version)
accessLevelRestricted
Identifiers
ISSN of the journal0363-9762
134views
0downloads

Technical informations

Creation07/16/2021 1:08:00 PM
First validation07/16/2021 1:08:00 PM
Update time03/16/2023 1:26:48 AM
Status update03/16/2023 1:26:47 AM
Last indexation01/17/2024 2:25:47 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack