1 INTRODUCTION

2 BACKGROUND  
2.1 Related Survey  
2.2 Unconditional and Conditional Generative Models  
2.3 Generative Adversarial Networks  
2.4 Autoencoders  
2.5 Autoregressive Models  
2.6 Advantages and Disadvantages of Different Generative Models  
2.7 Challenges of 3D Generative Models

3 3D BRAIN AND HEART APPLICATIONS OF GENERATIVE MODELS  
3.1 Unconditional Synthesis  
3.2 Classification  
3.3 Conditional Synthesis  
3.4 Segmentation  
3.5 Denoising  
3.6 Detection  
3.7 Registration

4 DISCUSSION AND FUTURE DIRECTIONS  
4.1 Few-shot Learning  
4.2 Self-supervised Learning  
4.3 Efficient 3D Network Architecture  
4.4 Denoising Diffusion Probabilistic Model (DDPM)  
4.5 Trustworthy, Explainability, and Potential Bias

5 CONCLUSION