mlsanityData to 14 Oct 2026

Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

arXiv 2306.02986 ↗First posted 5 Jun 2023eess.IV cs.CV cs.LGcited by 19

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80% - 90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 57 references that are in the map. First posted on arXiv 5 Jun 2023.

Builds on

29 of 57 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images12 May
  2. 2023DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models26 Apr

Other references · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  6. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  7. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  8. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  9. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  10. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  11. 2023Extracting Training Data from Diffusion Modelscited by 732
  12. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
15 more
  1. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  2. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 424
  3. 2023Synthetic Data from Diffusion Models Improves ImageNet Classificationcited by 327
  4. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  5. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  6. 2018A Note on the Inception Scorecited by 226
  7. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  8. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 147
  9. 2018GAN Augmentation: Augmenting Training Data using Generative Adversarial Networkscited by 107
  10. 2021SinGAN-Seg: Synthetic training data generation for medical image segmentationcited by 43
  11. 2022Can segmentation models be trained with fully synthetically generated data?cited by 37
  12. 2017Synthetic Medical Images from Dual Generative Adversarial Networkscited by 32
  13. 2020Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADEcited by 9
  14. 2021Ensembles of GANs for synthetic training data generationcited by 6
  15. 2022Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?cited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 10; 2026: 4.

19 papers.

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  1. 2025BRISC: Annotated Dataset for Brain Tumor Segmentation and Classificationcited by 15
  2. 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
  3. 2025Diffusion-empowered AutoPrompt MedSAMcited by 10
  4. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  5. 2023Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial contextcited by 7
  6. 2023Synthetic Tumor Manipulation: With Radiomics Featurescited by 2
  7. 2024Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Modelscited by 2
  8. 2025Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging?cited by 2
  9. 2025Diffusion-Driven Generation of Minimally Preprocessed Brain MRIcited by 2
  10. 2026TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growthcited by 2
  11. 2025Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomographycited by 1
  12. 2025Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentationcited by 1
7 more
  1. 2026Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025cited by 1
  2. 2025VITON-DRR: Details Retention Virtual Try-on via Non-rigid Registrationcited by 0
  3. 2025Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Networkcited by 0
  4. 2025Assessment of Using Synthetic Data in Brain Tumor Segmentationcited by 0
  5. 2025ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domaincited by 0
  6. 2026Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction modelscited by 0
  7. 2026FairGen: Preference-Aligned Diffusion for Demographically Equitable Medical Image Synthesiscited by 0

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  1. 2023A 3D generative model of pathological multi-modal MR images and segmentations
  2. 2025Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
  3. 2022Brain Imaging Generation with Latent Diffusion Models
  4. 2023Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation
  5. 2024Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models
  6. 2024Diffusion-Based Approaches in Medical Image Generation and Analysis
  7. 2024Cross-conditioned Diffusion Model for Medical Image to Image Translation
  8. 2025Deep Generative Model-Based Generation of Synthetic Individual-Specific Brain MRI Segmentations