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

• URL: http://arxiv.org/abs/2502.21187v2
• Date: Tue, 04 Mar 2025 12:18:40 GMT
• Title: SYN-LUNGS: Towards Simulating Lung Nodules with Anatomy-Informed Digital Twins for AI Training
• Authors: Fakrul Islam Tushar, Lavsen Dahal, Cindy McCabe, Fong Chi Ho, Paul Segars, Ehsan Abadi, Kyle J. Lafata, Ehsan Samei, Joseph Y. Lo,
• Abstract summary: Generative models for lung cancer screening are limited by data scarcity, impacting generalizability and clinical applicability.<n>We introduce SYN-LUNGS, a framework for generating high-quality 3D CT images with detailed annotations.<n>The dataset includes 3,072 nodule images from 1,044 simulated CT scans, with 512 lesions and 174 digital twins.
• Score: 1.4820790252910163
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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