mlsanityData to 14 Oct 2026

3D Brain and Heart Volume Generative Models: A Survey

arXiv 2210.05952 ↗First posted 12 Oct 2022eess.IV cs.CVcited by 4

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Abstract (arXiv)

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This paper provides a comprehensive survey of generative models for three-dimensional (3D) volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task and also suggest potential future directions. A list of the latest publications will be updated on Github to keep up with the rapid influx of papers at https://github.com/csyanbin/3D-Medical-Generative-Survey.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 118 of its 261 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

118 of 261 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

4 papers.

  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 273
  2. 2025TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiencycited by 8
  3. 2023fRegGAN with K-space Loss Regularization for Medical Image Translationcited by 3
  4. 2026Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomycited by 1