Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest

TL;DR

This paper introduces CRF-GAN, a memory-efficient 3D medical image synthesis model that outperforms the state-of-the-art in quality, speed, and resource usage, validated through quantitative metrics and radiologist assessments.

Contribution

The study presents a novel CRF-GAN architecture that improves 3D medical image synthesis efficiency and quality, combining conditional random fields with a two-step generation process.

Findings

CRF-GAN achieved lower FID and MMD scores than HA-GAN.

Radiologists preferred CRF-GAN generated images in 2AFC tests.

CRF-GAN used 9.34% less memory and trained 14.6% faster.

Abstract

Generative Adversarial Networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated data for training Artificial Intelligence systems. This study introduces CRF-GAN, a novel memory-efficient GAN architecture that enhances structural consistency in 3D medical image synthesis. Integrating Conditional Random Fields within a two-step generation process allows CRF-GAN improving spatial coherence while maintaining high-resolution image quality. The model's performance is evaluated against the state-of-the-art hierarchical (HA)-GAN model. Materials and Methods: We evaluate the performance of CRF-GAN against the HA-GAN model. The comparison between the two models was made through a quantitative evaluation, using FID and MMD metrics, and a qualitative evaluation, through a two-alternative forced choice (2AFC) test completed by a pool of 12 resident radiologists, to assess the realism of the generated images. Results: CRF-GAN outperformed HA-GAN with lower FID and MMD scores, indicating better image fidelity. The 2AFC test showed a significant preference for images generated by CRF-Gan over those generated by HA-GAN. Additionally, CRF-GAN demonstrated 9.34% lower memory usage and achieved up to 14.6% faster training speeds, offering substantial computational savings. Discussion: CRF-GAN model successfully generates high-resolution 3D medical images with non-inferior quality to conventional models, while being more memory-efficient and faster. The key objective was not only to lower the computational cost but also to reallocate the freed-up resources towards the creation of higher-resolution 3D imaging, which is still a critical factor limiting their direct clinical applicability. Moreover, unlike many previous studies, we combined qualitative and quantitative assessments to obtain a more holistic feedback on the model's performance.