Fiche publication
Date publication
août 2023
Journal
Diagnostics (Basel, Switzerland)
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HOEFFEL Christine
Tous les auteurs :
Mulliez D, Poncelet E, Ferret L, Hoeffel C, Hamet B, Dang LA, Laurent N, Ramette G
Lien Pubmed
Résumé
Uterus measurements are useful for assessing both the treatment and follow-ups of gynaecological patients. The aim of our study was to develop a deep learning (DL) tool for fully automated measurement of the three-dimensional size of the uterus on magnetic resonance imaging (MRI). In this single-centre retrospective study, 900 cases were included to train, validate, and test a VGG-16/VGG-11 convolutional neural network (CNN). The ground truth was manual measurement. The performance of the model was evaluated using the objective key point similarity (OKS), the mean difference in millimetres, and coefficient of determination R. The OKS of our model was 0.92 (validation) and 0.96 (test). The average deviation and R coefficient between the AI measurements and the manual ones were, respectively, 3.9 mm and 0.93 for two-point length, 3.7 mm and 0.94 for three-point length, 2.6 mm and 0.93 for width, 4.2 mm and 0.75 for thickness. The inter-radiologist variability was 1.4 mm. A three-dimensional automated measurement was obtained in 1.6 s. In conclusion, our model was able to locate the uterus on MRIs and place measurement points on it to obtain its three-dimensional measurement with a very good correlation compared to manual measurements.
Mots clés
MRI, artificial intelligence, convolutional neural network, deep learning, measurement, uterus
Référence
Diagnostics (Basel). 2023 08 12;13(16):
