用深度生成模型合成3D医学图像，提升诊断与治疗规划能力
Deep Generative Models for 3D Medical Image Synthesis
- 基于VAE、GAN和扩散模型实现3D医学图像生成
- 支持无条件与条件生成，可完成图像翻译与重建任务
- 适合医学影像研究者与算法开发人员参考
深度生成模型已成为合成逼真医学图像的强大工具，推动了医学图像分析、疾病诊断和治疗规划的进步。本章探讨用于3D医学图像合成的各类深度生成模型，重点包括变分自编码器（VAEs）、生成对抗网络（GANs）和去噪扩散模型（DDMs）。文章阐述这些模型的基本原理、最新进展及其优缺点，并分析其在图像到图像转换、图像重建等临床相关任务中的应用。此外，还综述了评估图像保真度、多样性、实用性和隐私性的常用指标，并概述了当前领域的挑战。
原文摘要 · Abstract (English)
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.
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