MMI-GAN: Multi Medical Imaging Translation using Generative Adversarial Network

• Eduardo Felipe de Souza UFAL
• Marcelo Costa Oliveira UFAL

DOI: https://doi.org/10.5753/sbcas.2021.16105

Abstract

Medical image translation is considered a new frontier in the field of medical image analysis, with great potential for application. However, existing approaches have limited scalability and robustness in handling more than two image domains. To solve these limitations, we developed MMI-GAN, a new approach for translation between multiple image domains, capable of translating intermodal (CT and MR) and intramodal (PD, T1 and T2) images using only one generator and one discriminator. The images translated by MMI-GAN managed to obtain MAE of 5.79, PSNR of 27.39, MI of 1.43 and SSIM of 0.90. Their results were often statically comparable or superior to the state of the art.

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