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Generative Models for Synthesizing Anatomical Plausible 3D Medical Images

In: Generative Machine Learning Models in Medical Image Computing

Author

Listed:
  • Wei Peng

    (Stanford University, Department of Psychiatry & Behavioral Sciences)

  • Kilian M. Pohl

    (Stanford University, Department of Psychiatry & Behavioral Sciences
    Stanford University, Department of Electrical Engineering)

Abstract

Deep learning methods trained on 3D medical images typically do not generalize well as training data are relatively homogenous and small. One way to potentially overcome this issue is creating realistic-looking 3D medical images using generative models. This chapter describes the fundamental principles and architectures of generative models used for this purpose, such as those based on generative adversarial networks (GANs) and diffusion probabilistic models (DPMs). The chapter also reviews evaluation techniques for measuring the quality of synthetic medical images, including the evaluation of the biological plausibility of the anatomy displayed.

Suggested Citation

  • Wei Peng & Kilian M. Pohl, 2025. "Generative Models for Synthesizing Anatomical Plausible 3D Medical Images," Springer Books, in: Le Zhang & Chen Chen & Zeju Li & Greg Slabaugh (ed.), Generative Machine Learning Models in Medical Image Computing, chapter 0, pages 323-339, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-80965-1_16
    DOI: 10.1007/978-3-031-80965-1_16
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