CT-SGAN: Computed Tomography Synthesis GAN

TL;DR

CT-SGAN is a novel GAN-based model that synthesizes large-scale 3D CT scans from limited data, enhancing medical imaging datasets and improving lung nodule detection accuracy.

Contribution

The paper introduces CT-SGAN, a recurrent GAN that generates realistic 3D CT volumes from small datasets, addressing data scarcity and privacy issues in medical imaging.

Findings

Generated CT scans have high fidelity based on quantitative metrics.

Pre-training on synthetic data improves lung nodule detection accuracy.

CT-SGAN effectively enlarges datasets for medical imaging tasks.

Abstract

Diversity in data is critical for the successful training of deep learning models. Leveraged by a recurrent generative adversarial network, we propose the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes ($\geq 224\times224\times224$) when trained on a small dataset of chest CT-scans. CT-SGAN offers an attractive solution to two major challenges facing machine learning in medical imaging: a small number of given i.i.d. training data, and the restrictions around the sharing of patient data preventing to rapidly obtain larger and more diverse datasets. We evaluate the fidelity of the generated images qualitatively and quantitatively using various metrics including Fr\'echet Inception Distance and Inception Score. We further show that CT-SGAN can significantly improve lung nodule detection accuracy by pre-training a classifier on a vast amount of synthetic data.