MMI-GAN: Multi Medical Imaging Translation using Generative Adversarial Network
Resumo
A tradução de imagens médicas é considerada uma nova fronteira no campo da análise de imagens médicas, com grande potencial de aplicação. No entanto, as abordagens existentes têm escalabilidade e robustez limitadas no manuseio de mais de dois domínios de imagens. Para resolver essas limitações, desenvolvemos a MMI-GAN, uma nova abordagem para tradução entre múltiplos domínios de imagem, capaz de traduzir imagens intermodais (TC e RM) e intramodais (PD, T1 e T2) usando apenas um gerador e um discriminador. As imagens traduzidas pela MMI-GAN conseguiram obter MAE de 5.79, PSNR de 27.39, MI de 1.43 e SSIM de 0.90. Os seus resultados foram por muitas vezes estaticamente equiparáveis ou superiores ao estado da arte.Referências
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Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134.
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Tanaka, H., Hayashi, S., Ohtakara, K., Hoshi, H., and Iida, T. (2011). Usefulness of ct- mri fusion in radiotherapy planning for localized prostate cancer. Journal of radiation research, pages 1109280230–1109280230.
Yi, X., Walia, E., and Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical image analysis, 58:101552.
Yi, Z., Zhang, H., Tan, P., and Gong, M. (2017). Dualgan: Unsupervised dual learning for image-to-image translation. In Proceedings of the IEEE international conference on computer vision, pages 2849–2857.
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017). Unpaired image-to-image transla- tion using cycle-consistent adversarial networks. In Proceedings of the IEEE interna- tional conference on computer vision, pages 2223–2232.
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J. (2018). Stargan: Unied generative adversarial networks for multi-domain image-to-image translation. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 8789–8797.
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134.
Krupa, K. and Bekiesiínska-Figatowska, M. (2015). Artifacts in magnetic resonance imaging. Polish journal of radiology, 80:93.
Liu, F. (2019). Susan: segment unannotated image structure using adversarial network. Magnetic resonance in medicine, 81(5):3330–3345.
Tanaka, H., Hayashi, S., Ohtakara, K., Hoshi, H., and Iida, T. (2011). Usefulness of ct- mri fusion in radiotherapy planning for localized prostate cancer. Journal of radiation research, pages 1109280230–1109280230.
Yi, X., Walia, E., and Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical image analysis, 58:101552.
Yi, Z., Zhang, H., Tan, P., and Gong, M. (2017). Dualgan: Unsupervised dual learning for image-to-image translation. In Proceedings of the IEEE international conference on computer vision, pages 2849–2857.
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A. (2017). Unpaired image-to-image transla- tion using cycle-consistent adversarial networks. In Proceedings of the IEEE interna- tional conference on computer vision, pages 2223–2232.
Publicado
15/06/2021
Como Citar
SOUZA, Eduardo Felipe de; OLIVEIRA, Marcelo Costa.
MMI-GAN: Multi Medical Imaging Translation using Generative Adversarial Network. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 79-84.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas.2021.16105.