Generation of synthetic rat brain MRI scans with a 3D enhanced alpha generative adversarial network

• Autor(a) principal: Ferreira, André
• Data de Publicação: 2022
• Outros Autores: Magalhães, Ricardo, Mériaux, Sébastien, Alves, Victor
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/79886
• topic: alpha generative adversarial network; data augmentation; data augmentation; synthetic data; MRI rat brain; alpha generative adversarial network; Ciências Naturais::Ciências da Computação e da Informação; Engenharia e Tecnologia::Engenharia Médica; Science & Technology

Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session. Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. To overcome these challenges, an adaptation of the alpha Generative Adversarial Networks (GANs) architecture was used to test its ability to generate realistic 3D MRI scans of the rat brain in silico. As far as the authors are aware, this was the first time a GAN-based approach was used to generate synthetic MRI data of the rat brain. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test. The last two tests proved the realism and applicability of the generated scans to real problems. Therefore, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans, and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation.