Improved $α$-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning
This addresses data scarcity in medical imaging for applications like radiosurgery treatment planning, but it is incremental as it builds on existing α-GAN methods.
The paper tackled generating connected 3D volumes using an improved 3D α-GAN architecture, achieving realistic synthetic 3D tumor volumes with similar geometrical characteristics to training data.
Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiation therapy can benefit greatly from synthetic data generation due to data scarcity in the domain. However, medical image data is typically kept in 3D space, and generative models suffer from the curse of dimensionality issues in generating such synthetic data. In this paper, we investigate the potential of GANs for generating connected 3D volumes. We propose an improved version of 3D $α$-GAN by incorporating various architectural enhancements. On a synthetic dataset of connected 3D spheres and ellipsoids, our model can generate fully connected 3D shapes with similar geometrical characteristics to that of training data. We also show that our 3D GAN model can successfully generate high-quality 3D tumor volumes and associated treatment specifications (e.g., isocenter locations). Similar moment invariants to the training data as well as fully connected 3D shapes confirm that improved 3D $α$-GAN implicitly learns the training data distribution, and generates realistic-looking samples. The capability of improved 3D $α$-GAN makes it a valuable source for generating synthetic medical image data that can help future research in this domain.