Generative Models for Synthesizing Anatomical Plausible 3D Medical Images

Wei Peng () and Kilian M. Pohl ()
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Wei Peng: Stanford University, Department of Psychiatry & Behavioral Sciences
Kilian M. Pohl: Stanford University, Department of Psychiatry & Behavioral Sciences

Chapter Chapter 16 in Generative Machine Learning Models in Medical Image Computing, 2025, pp 323-339 from Springer

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-80965-1_16

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DOI: 10.1007/978-3-031-80965-1_16

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