Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing.
Brain atrophy
Generative adversarial network (GAN)
Latent space
Magnetic resonance imaging (MRI)
Multiple sclerosis (MS)
Journal
Zeitschrift fur medizinische Physik
ISSN: 1876-4436
Titre abrégé: Z Med Phys
Pays: Germany
ID NLM: 100886455
Informations de publication
Date de publication:
23 Dec 2023
23 Dec 2023
Historique:
received:
28
02
2023
revised:
15
11
2023
accepted:
01
12
2023
medline:
25
12
2023
pubmed:
25
12
2023
entrez:
24
12
2023
Statut:
aheadofprint
Résumé
Multiple sclerosis (MS) is a complex neurodegenerative disorder that affects the brain and spinal cord. In this study, we applied a deep learning-based approach using the StyleGAN model to explore patterns related to MS and predict disease progression in magnetic resonance images (MRI). We trained the StyleGAN model unsupervised using T Our results show that MS progression can be simulated by manipulating MR images in the latent space, as evidenced by brain volume loss on both T Overall, this study demonstrates the potential of the StyleGAN model in medical imaging to study image markers and to shed more light on the relationship between brain atrophy and MS progression through corresponding manipulations in the latent space.
Identifiants
pubmed: 38143166
pii: S0939-3889(23)00148-4
doi: 10.1016/j.zemedi.2023.12.001
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier GmbH.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.