Multiparametric magnetic resonance image synthesis using generative adversarial networks

Haarburger C, Horst N, Truhn D, Broeckmann M, Schrading S, Kuhl C, Merhof D (2019)

Publication Type: Conference contribution

Publication year: 2019

Publisher: Eurographics Association

Pages Range: 11-15

Conference Proceedings Title: Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019

Event location: Brno, CZE

ISBN: 9783038680819

DOI: 10.2312/vcbm.20191226

Abstract

Generative adversarial networks have been shown to alleviate the problem of limited training data for supervised learning problems in medical image computing. However, most generative models for medical images focus on image-to-image translation rather than de novo image synthesis. In many clinical applications, image acquisition is multiparametric, i.e. includes contrast-enchanced or diffusion-weighted imaging. We present a generative adversarial network that synthesizes a sequence of temporally consistent contrast-enhanced breast MR image patches. Performance is evaluated quantitatively using the Fréchet Inception Distance, achieving a minimum FID of 21.03. Moreover, a qualitative human reader test shows that even a radiologist cannot differentiate between real and fake images easily.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Universitätsklinikum Aachen (UKA) DE Germany (DE)

How to cite

APA:

Haarburger, C., Horst, N., Truhn, D., Broeckmann, M., Schrading, S., Kuhl, C., & Merhof, D. (2019). Multiparametric magnetic resonance image synthesis using generative adversarial networks. In Barbora Kozlikova, Renata Georgia Raidou (Eds.), Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019 (pp. 11-15). Brno, CZE: Eurographics Association.

MLA:

Haarburger, Christoph, et al. "Multiparametric magnetic resonance image synthesis using generative adversarial networks." Proceedings of the 2019 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2019, Brno, CZE Ed. Barbora Kozlikova, Renata Georgia Raidou, Eurographics Association, 2019. 11-15.

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