This dataset contains synthetic data created by a generative AI model.
This dataset is a collection of synthetic images generated by 5 generative models (Progressive GAN, StyleGAN1, StyleGAN2, StyleGAN3, diffusion model) trained on the BraTS 2020 and 2021 datasets [1,2,3,4,5] (which share MR volumes from brain tumor patients and the corresponding tumor annotations). The trained generative models are also shared in this dataset. See our recent work  for more information, and a comparison of training segmentation networks with real and synthetic images.
Keywords: Radiology, Annotated, Brain, MRI, Tumor, Synthetic, Generative, GAN, Diffusion model.
Sample images with reduced image quality. Please click to preview.
The tumor annotation image for each 4-channel MR image was generated by each generative model.
Free for use in legal and ethical medical diagnostics research and education. Please contact the dataset provider for terms of access.
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AIDA BY license
Copyright 2023 Linköping University, Muhammad Usman Akbar, Linköping University, Anders Eklund
Permission to use, copy, modify, and/or distribute this data within Analytic Imaging Diagnostics Arena (AIDA) for the purpose of medical diagnostics research with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies, and that publications resulting from the use of this data cite the following works:
Muhammad Usman Akbar and Anders Eklund (2023) Synthetic brain tumor images from GANs and diffusion models doi:10.23698/aida/synthetic/brgandi.
U.Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification, arXiv:2107.02314, 2021.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features, Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection, The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection, The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.GJQ7R0EF
Akbar, M. U., Larsson, M., & Eklund, A. (2023). Brain tumor segmentation using synthetic MR images–A comparison of GANs and diffusion models. arXiv:2306.02986.
THE DATA IS PROVIDED “AS IS” AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS DATA INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR CHARACTERISTICS OF THIS DATA.