mlsanityData to 14 Oct 2026

Deep Generative Models for 3D Medical Image Synthesis

arXiv 2410.17664 ↗First posted 23 Oct 2024eess.IV cs.CVcited by 14

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Abstract (arXiv)

Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical images, driving advances in medical image analysis, disease diagnosis, and treatment planning. This chapter explores various deep generative models for 3D medical image synthesis, with a focus on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Models (DDMs). We discuss the fundamental principles, recent advances, as well as strengths and weaknesses of these models and examine their applications in clinically relevant problems, including unconditional and conditional generation tasks like image-to-image translation and image reconstruction. We additionally review commonly used evaluation metrics for assessing image fidelity, diversity, utility, and privacy and provide an overview of current challenges in the field.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 89 of its 140 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

89 of 140 references.

References · 89

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 5.

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  1. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generationcited by 37
  2. 2024cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesiscited by 19
  3. 2024vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentationcited by 18
  4. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  5. 2026EchoLVFM: One-Step Video Generation via Latent Flow Matching for Echocardiogram Synthesiscited by 3
  6. 2025Evaluation of 3D Counterfactual Brain MRI Generationcited by 2
  7. 2026Treatment-Conditioned Diffusion for Forecasting Neurodegenerative Disease Progressioncited by 1
  8. 2026Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histologycited by 1
  9. 2026Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomycited by 1
  10. 2025Efficient and robust 3D blind harmonization for large domain gapscited by 0
  11. 2025MInDI-3D: Iterative Deep Learning in 3D for Sparse-view Cone Beam Computed Tomographycited by 0
  12. 2025Towards Diagnostic Quality Flat-Panel Detector CT Imaging Using Diffusion Modelscited by 0
2 more
  1. 2025Deep Generative Models for Enhanced Vitreous OCT Imagingcited by 0
  2. 2026Structural MRI Synthesis for Alzheimer's Disease via Conditional Diffusion on Anatomical Maskscited by 0

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  3. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
  4. 2024Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling
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