SYN-LUNGS: Towards Simulating Lung Nodules with Anatomy-Informed Digital Twins for AI Training

TL;DR

SYN-LUNGS is a novel framework that generates high-quality, annotated 3D lung CT images using anatomy-informed digital twins, enhancing AI training for lung nodule detection and classification.

Contribution

It introduces a comprehensive simulation pipeline combining digital twins, lesion variability, and CT image formation to create a large, diverse dataset for AI model training.

Findings

Models trained on combined clinical and simulated data outperform clinical-only models.

Simulated data improves detection accuracy by 10%.

Enhanced model robustness in nodule segmentation and classification.

Abstract

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.