SYN-LUNGS: Towards Simulating Lung Nodules with Anatomy-Informed Digital Twins for AI Training
This addresses data limitations for AI in medical imaging, particularly for rare diseases, but is incremental as it builds on existing simulation methods.
The paper tackled the problem of data scarcity in AI models for lung cancer screening by introducing SYN-LUNGS, a framework for generating high-quality 3D CT images with detailed annotations, resulting in models trained on clinical plus simulated data outperforming clinical-only models with a 10% improvement in detection and 2-9% in segmentation and classification.
AI models for lung cancer screening are limited by data scarcity, impacting generalizability and clinical applicability. Generative models address this issue but are constrained by training data variability. We introduce SYN-LUNGS, a framework for generating high-quality 3D CT images with detailed annotations. SYN-LUNGS integrates XCAT3 phantoms for digital twin generation, X-Lesions for nodule simulation (varying size, location, and appearance), and DukeSim for CT image formation with vendor and parameter variability. The dataset includes 3,072 nodule images from 1,044 simulated CT scans, with 512 lesions and 174 digital twins. Models trained on clinical + simulated data outperform clinical only models, achieving 10% improvement in detection, 2-9% in segmentation and classification, and enhanced synthesis. By incorporating anatomy-informed simulations, SYN-LUNGS provides a scalable approach for AI model development, particularly in rare disease representation and improving model reliability.