Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis

• URL: http://arxiv.org/abs/2502.15204v1
• Date: Fri, 21 Feb 2025 04:38:27 GMT
• Title: Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
• Authors: Yifan Jiang, Yannick Lemaréchal, Josée Bafaro, Jessica Abi-Rjeile, Philippe Joubert, Philippe Després, Venkata Manem,
• Abstract summary: Lung-DDPM is a thoracic CT image synthesis approach that effectively generates high-fidelity 3D synthetic CT images.<n>Our results suggest that the proposed method outperforms other state-of-the-art generative models in image quality evaluation and downstream lung nodule segmentation tasks.<n>The experimental results highlight Lung-DDPM's potential for a broader range of medical imaging applications.
• Score: 3.433052805056497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of artificial intelligence (AI), AI-assisted medical imaging analysis demonstrates remarkable performance in early lung cancer screening. However, the costly annotation process and privacy concerns limit the construction of large-scale medical datasets, hampering the further application of AI in healthcare. To address the data scarcity in lung cancer screening, we propose Lung-DDPM, a thoracic CT image synthesis approach that effectively generates high-fidelity 3D synthetic CT images, which prove helpful in downstream lung nodule segmentation tasks. Our method is based on semantic layout-guided denoising diffusion probabilistic models (DDPM), enabling anatomically reasonable, seamless, and consistent sample generation even from incomplete semantic layouts. Our results suggest that the proposed method outperforms other state-of-the-art (SOTA) generative models in image quality evaluation and downstream lung nodule segmentation tasks. Specifically, Lung-DDPM achieved superior performance on our large validation cohort, with a Fr\'echet inception distance (FID) of 0.0047, maximum mean discrepancy (MMD) of 0.0070, and mean squared error (MSE) of 0.0024. These results were 7.4$\times$, 3.1$\times$, and 29.5$\times$ better than the second-best competitors, respectively. Furthermore, the lung nodule segmentation model, trained on a dataset combining real and Lung-DDPM-generated synthetic samples, attained a dice coefficient (Dice) of 0.3914 and sensitivity of 0.4393. This represents 8.8\% and 18.6\% improvements in DICE and sensitivity compared to the model trained solely on real samples. The experimental results highlight Lung-DDPM's potential for a broader range of medical imaging applications, such as general tumor segmentation, cancer survival estimation, and risk prediction.

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