3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images (2107.09700v1)

Abstract: Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as, image enhancement and disease progression modeling. However, current GAN technologies for 3D medical image synthesis need to be significantly improved to be readily adapted to real-world medical problems. In this paper, we extend the state-of-the-art StyleGAN2 model, which natively works with two-dimensional images, to enable 3D image synthesis. In addition to the image synthesis, we investigate the controllability and interpretability of the 3D-StyleGAN via style vectors inherited form the original StyleGAN2 that are highly suitable for medical applications: (i) the latent space projection and reconstruction of unseen real images, and (ii) style mixing. We demonstrate the 3D-StyleGAN's performance and feasibility with ~12,000 three-dimensional full brain MR T1 images, although it can be applied to any 3D volumetric images. Furthermore, we explore different configurations of hyperparameters to investigate potential improvement of the image synthesis with larger networks. The codes and pre-trained networks are available online: https://github.com/sh4174/3DStyleGAN.

• The paper introduces 3D-StyleGAN, an adaptation of StyleGAN2 using 3D convolutions and memory management techniques to synthesize high-quality 3D medical images.
• Key methodological contributions include the use of 3D operations, reduced feature map depths, and adaptation of latent space projection for improved synthesis quality.
• 3D-StyleGAN offers significant potential for medical research and clinical settings, enabling advanced high-resolution imaging, progressive disease modeling, and enhanced image manipulation.

Overview of 3D-StyleGAN: A Style-Based Generative Adversarial Network for Three-Dimensional Medical Image Synthesis

The paper introduces 3D-StyleGAN, an innovative extension of the StyleGAN2 architecture aimed at synthesizing three-dimensional (3D) medical images, specifically focusing on brain MR T1 scans. This research addresses the limitations of current Generative Adversarial Network (GAN) technologies in modeling 3D medical images which entail higher memory demands and voxel counts compared to conventional 2D images. By adapting StyleGAN2 to operate with 3D convolutions and noise inputs, the authors have successfully demonstrated high-quality synthesis of volumetric medical data.

Methodological Contributions

The authors reconfigured StyleGAN2's style-based approach to handle 3D representations, replacing 2D operations with 3D equivalents while adjusting filter depths and latent vector sizes to accommodate computational constraints. Their modified network architecture allows for significant enhancements in the quality and coherence of synthesized 3D images.

Key aspects of 3D-StyleGAN include:

• 3D Operations: Transition from 2D to 3D operations in convolution layers, noise mapping, and up/down-sampling.
• Memory Management: Reduction in feature map depths and latent vector size to manage increased voxel data efficiently.
• Latent Space Projection: Adaptation of image embedding to project unseen real images back into the latent space for reconstruction with enhanced fidelity.
• Style Mixing: A technique to exchange style vectors across resolution levels to control anatomical variability within generated images.

Experimental Evaluation

Experiments conducted using a dataset of approximately 12,000 brain MR images show the robustness of 3D-StyleGAN across various settings. The paper details configurations with different resolutions and filter depths, demonstrating high-quality image generation with layers configured for enhanced anatomical fidelity. Evaluation metrics such as bMMD², MS-SSIM, and Fréchet Inception Distance (FID) adapted for slice-wise analysis on axial, coronal, and sagittal slices indicate promising results in perceptual diversity and generation quality.

Implications and Future Directions

The introduction of 3D-StyleGAN has profound implications for clinical and research settings, enabling advanced applications such as high-resolution medical imaging, progressive modeling related to disease states, and enhanced image manipulation tools for biomedical research. Future enhancements could focus on developing efficient memory usage strategies and refining 3D evaluation metrics, addressing current limitations in discriminating perceptual qualities effectively.

Looking forward, the research community may explore:

• Explicit Metrics for 3D Image Quality: Developing metrics independent of 2D pretrained models to assess perceptual qualities in 3D images comprehensively.
• Scalability and Network Optimization: Investigating architectures that can support full-resolution 1mm isotropic scans within reasonable computational limits.
• Diverse Medical Applications: Application to various types of medical scans, not limited to brain imagery, optimizing and validating across modalities and resolutions.

Conclusion

3D-StyleGAN exemplifies a substantial enhancement in GAN technology tailored to 3D medical imaging, establishing a foothold in generative modeling for volumetric data. The paper paves the way for future explorations into leveraging style-based generators for complex biomedical imaging, offering both theoretical insights and practical advancements. The availability of open-source code and pretrained models presents opportunities for the broader scientific community to engage with and build upon this work in ongoing research and development efforts.