Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks

TL;DR

This paper introduces a two-stage GAN framework that improves the diversity, controllability, and quality of synthetic lung nodule CT images, leading to better detection model performance.

Contribution

The novel TSGAN framework decouples structure and texture generation, enhancing synthetic data diversity and controllability for lung nodule imaging.

Findings

Detection accuracy improved by 4.6%.

Mean Average Precision (mAP) increased by 4%.

Synthetic images quality was significantly enhanced.

Abstract

The limited sample size and insufficient diversity of lung nodule CT datasets severely restrict the performance and generalization ability of detection models. Existing methods generate images with insufficient diversity and controllability, suffering from issues such as monotonous texture features and distorted anatomical structures. Therefore, we propose a two-stage generative adversarial network (TSGAN) to enhance the diversity and spatial controllability of synthetic data by decoupling the morphological structure and texture features of lung nodules. In the first stage, StyleGAN is used to generate semantic segmentation mask images, encoding lung nodules and tissue backgrounds to control the anatomical structure of lung nodule images; The second stage uses the DL-Pix2Pix model to translate the mask map into CT images, employing local importance attention to capture local features, while utilizing dynamic weight multi-head window attention to enhance the modeling capability of lung nodule texture and background. Compared to the original dataset, the accuracy improved by 4.6% and mAP by 4% on the LUNA16 dataset. Experimental results demonstrate that TSGAN can enhance the quality of synthetic images and the performance of detection models.