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Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images

Hristina Uzunova; Jan Ehrhardt; Fabian Jacob; Alex Frydrychowicz; Heinz Handels
In: Dinggang Shen; Tianming Liu; Terry M. Peters; Lawrence H. Staib; Caroline Essert; Sean Zhou; Pew-Thian Yap; Ali Khan (Hrsg.). Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019. Medical Image Computing and Computer Assisted Intervention (MICCAI-2019), October 13-17, Shenzhen, China, Pages 112-120, ISBN 978-3-030-32226-7, Springer International Publishing, 2019.


Currently generative adversarial networks (GANs) are rarely applied to medical images of large sizes, especially 3D volumes, due to their large computational demand. We propose a novel multi-scale patch-based GAN approach to generate large high resolution 2D and 3D images. Our key idea is to first learn a low-resolution version of the image and then generate patches of successively growing resolutions conditioned on previous scales. In a domain translation use-case scenario, 3D thorax CTs of size $$512^3$$5123and thorax X-rays of size $$2048^2$$20482are generated and we show that, due to the constant GPU memory demand of our method, arbitrarily large images of high resolution can be generated. Moreover, compared to common patch-based approaches, our multi-resolution scheme enables better image quality and prevents patch artifacts.