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


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 and thorax X-rays of size 2048 2 are 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.

Original languageEnglish
Title of host publicationMICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Number of pages9
Place of PublicationCham
PublisherSpringer Verlag
Publication date10.10.2019
ISBN (Print)978-3-030-32225-0
ISBN (Electronic)978-3-030-32226-7
Publication statusPublished - 10.10.2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China
Duration: 13.10.201917.10.2019
Conference number: 232939

Research Areas and Centers

  • Academic Focus: Biomedical Engineering


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