mlsanityData to 14 Oct 2026

Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks

arXiv 1908.02498 ↗First posted 7 Aug 2019eess.IV cs.CVcited by 65

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Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

As deep learning is showing unprecedented success in medical image analysis tasks, the lack of sufficient medical data is emerging as a critical problem. While recent attempts to solve the limited data problem using Generative Adversarial Networks (GAN) have been successful in generating realistic images with diversity, most of them are based on image-to-image translation and thus require extensive datasets from different domains. Here, we propose a novel model that can successfully generate 3D brain MRI data from random vectors by learning the data distribution. Our 3D GAN model solves both image blurriness and mode collapse problems by leveraging alpha-GAN that combines the advantages of Variational Auto-Encoder (VAE) and GAN with an additional code discriminator network. We also use the Wasserstein GAN with Gradient Penalty (WGAN-GP) loss to lower the training instability. To demonstrate the effectiveness of our model, we generate new images of normal brain MRI and show that our model outperforms baseline models in both quantitative and qualitative measurements. We also train the model to synthesize brain disorder MRI data to demonstrate the wide applicability of our model. Our results suggest that the proposed model can successfully generate various types and modalities of 3D whole brain volumes from a small set of training data.

Key facts. Cited by 65 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Aug 2019.

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Citing papers per year: 2021: 4; 2022: 14; 2023: 14; 2024: 8; 2025: 17; 2026: 8.

65 papers.

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  1. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 268
  2. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 86
  3. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesiscited by 56
  4. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generationcited by 54
  5. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  6. 2023MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Imagescited by 41
  7. 2023MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generationcited by 34
  8. 20213D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Imagescited by 32
  9. 2022Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Modelcited by 28
  10. 2022A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysiscited by 24
  11. 2022Morphology-preserving Autoregressive 3D Generative Modelling of the Braincited by 15
  12. 2022Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Frameworkcited by 14
38 more
  1. 2024Generative Enhancement for 3D Medical Imagescited by 14
  2. 2024Deep Generative Models for 3D Medical Image Synthesiscited by 14
  3. 2022FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomycited by 13
  4. 2023Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIscited by 12
  5. 2022Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encodercited by 8
  6. 2022Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Imagescited by 8
  7. 2023Multiscale Metamorphic VAE for 3D Brain MRI Synthesiscited by 8
  8. 2022Cross-Modality Neuroimage Synthesis: A Surveycited by 7
  9. 2022Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANscited by 7
  10. 2024Latent Causal Modeling for 3D Brain MRI Counterfactualscited by 7
  11. 2023Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networkscited by 6
  12. 2023Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculaturecited by 5
  13. 2025WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIscited by 5
  14. 20223D Brain and Heart Volume Generative Models: A Surveycited by 4
  15. 2023Deep Generative Networks for Heterogeneous Augmentation of Cranial Defectscited by 4
  16. 2024DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Modelscited by 4
  17. 2025BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesiscited by 4
  18. 2024Applications of interpretable deep learning in neuroimaging: a comprehensive reviewcited by 3
  19. 2024Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentationcited by 3
  20. 2022Improved α-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planningcited by 2
  21. 2023Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulationscited by 2
  22. 2023Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentationcited by 2
  23. 2025Towards Better Cephalometric Landmark Detection with Diffusion Data Generationcited by 2
  24. 2025From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Reviewcited by 2
  25. 2025Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generationcited by 2
  26. 2026Anatomically Guided Latent Diffusion for Brain MRI Progression Modelingcited by 2
  27. 2026Pixel-Level Residual Diffusion Transformer: Scalable 3D CT Volume Generationcited by 2
  28. 2021Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patientscited by 1
  29. 2021Data Augmentation For Medical MR Image Using Generative Adversarial Networkscited by 1
  30. 2022'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patientcited by 1
  31. 2023Method for Generating Synthetic Data Combining Chest Radiography Images with Tabular Clinical Information Using Dual Generative Modelscited by 1
  32. 2023A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancycited by 1
  33. 2025PRO: Projection Domain Synthesis for CT Imagingcited by 1
  34. 2025Integrating Anatomical Priors into a Causal Diffusion Modelcited by 1
  35. 2025Cycle Diffusion Model for Counterfactual Image Generationcited by 1
  36. 2026Optimizing 3D Diffusion Models for Medical Imaging via Multi-Scale Reward Learningcited by 1
  37. 2026Beyond Representation Learning: A Systematic Study of Joint-Embedding Predictive Generation for 3D Brain MRIcited by 1
  38. 2021Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GANcited by 0

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