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Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge - 04/01/23

Doi : 10.1016/j.diii.2022.09.005 
Sébastien Mulé a, b, , Littisha Lawrance c, Younes Belkouchi c, d, Valérie Vilgrain e, f, Maité Lewin g, h, Hervé Trillaud i, Christine Hoeffel j, k, Valérie Laurent l, Samy Ammari c, m, Eric Morand n, Orphée Faucoz n, Arthur Tenenhaus o, Anne Cotten p, q, Jean-François Meder r, s, Hugues Talbot d, Alain Luciani a, b, Nathalie Lassau c, m
a Medical Imaging Department, AP-HP, Henri Mondor University Hospital, Créteil 94000, France 
b INSERM, U955, Team 18, Créteil 94000, France 
c Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, Inserm, CNRS, CEA, BIOMAPS, UMR 1281, Université Paris-Saclay, Villejuif 94800, France 
d OPIS–Optimisation Imagerie et Santé, Inria, CentraleSupélec, CVN-Centre de Vision Numérique, Université Paris-Saclay, Gif-Sur-Yvette 91190, France 
e Department of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Hôpital Beaujon, Clichy 92110, France 
f CRI INSERM, Université Paris Cité, Paris 75018, France 
g Department of Radiology, AP-HP Hôpital Paul Brousse, Villejuif 94800, France 
h Faculté de Médecine, Université Paris-Saclay, Le Kremlin-Bicêtre 94270, France 
i CHU de Bordeaux, Department of Radiology, Université de Bordeaux, Bordeaux 33000, France 
j Department of Radiology, Reims University Hospital, Reims 51092, France 
k CRESTIC, University of Reims Champagne-Ardenne, Reims 51100, France 
l Department of Radiology, Nancy University Hospital, University of Lorraine, Vandoeuvre-ls-Nancy 54500, France 
m Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay, Villejuif 94800, France 
n Centre National d'Etudes Spatiales–CNES, Centre Spatial de Toulouse, Toulouse 31401 CEDEX 9 University, France 
o CentraleSupélec, Laboratoire des Signaux et Systèmes, Université Paris-Saclay, Gif-sur-Yvette 91190, France 
p Department of Musculoskeletal Radiology, Centre de Consultations Et D'imagerie de L'appareil Locomoteur, Lille 59037, France 
q Lille University School of Medicine, Lille, France 
r Department of Neuroimaging, Sainte-Anne Hospital, Paris 75013 University, France 
s Université Paris Cité, Paris 75006, France 

Corresponding author at: Medical Imaging Department, AP-HP, Henri Mondor University Hospital, Créteil 94000, France.Medical Imaging DepartmentAP-HPHenri Mondor University HospitalCréteil94000France

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Highlights

The SFR 2021 Artificial Intelligence Data Challenge focused on generative adversarial network (GAN)-based data augmentation techniques.
The aim was to create a synthetic dataset of 1000 macrotrabecular-massive hepatocellular carcinomas, which is a rare and aggressive subtype of hepatocellular carcinoma.
This data challenge demonstrates the ability of generative adversarial networks techniques to generate a large number of images from a small sample of rare malignant tumor MRI examinations.

El texto completo de este artículo está disponible en PDF.

Abstract

Purpose

The 2021 edition of the Artificial Intelligence Data Challenge was organized by the French Society of Radiology together with the Centre National d’Études Spatiales and CentraleSupélec with the aim to implement generative adversarial networks (GANs) techniques to provide 1000 magnetic resonance imaging (MRI) cases of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC), a rare and aggressive subtype of HCC, generated from a limited number of real cases from multiple French centers.

Materials and methods

A dedicated platform was used by the seven inclusion centers to securely upload their anonymized MRI examinations including all three cross-sectional images (one late arterial and one portal-venous phase T1-weighted images and one fat-saturated T2-weighted image) in compliance with general data protection regulation. The quality of the database was checked by experts and manual delineation of the lesions was performed by the expert radiologists involved in each center. Multidisciplinary teams competed between October 11th, 2021 and February 13th, 2022.

Results

A total of 91 MTM-HCC datasets of three images each were collected from seven French academic centers. Six teams with a total of 28 individuals participated in this challenge. Each participating team was asked to generate one thousand 3-image cases. The qualitative evaluation was performed by three radiologists using the Likert scale on ten randomly selected cases generated by each participant. A quantitative evaluation was also performed using two metrics, the Frechet inception distance and a leave-one-out accuracy of a 1-Nearest Neighbor algorithm.

Conclusion

This data challenge demonstrates the ability of GANs techniques to generate a large number of images from a small sample of imaging examinations of a rare malignant tumor.

El texto completo de este artículo está disponible en PDF.

Keywords : Artificial intelligence, Deep learning, Generative adversarial networks, Liver cancer, Magnetic resonance imaging

Abbreviations : AI, CNES, CNN, DL, FID, GANs, GDPR, HCC, MRI, MTM, SFR


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© 2022  Société française de radiologie. Publicado por Elsevier Masson SAS. Todos los derechos reservados.
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Vol 104 - N° 1

P. 43-48 - janvier 2023 Regresar al número
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