Enhancing Plaque Segmentation in CCTA with Prompt- Based Diffusion Data Augmentation

Abstract

Coronary computed tomography angiography (CCTA) is essential for non-invasive assessment of coronary artery disease (CAD). However, accurate segmentation of atherosclerotic plaques remains challenging due to data scarcity, severe class imbalance, and significant variability between calcified and non-calcified plaques. Inspired by DiffTumor’s tumor synthesis and PromptIR’s adaptive restoration framework, we introduce PromptLesion, a prompt-conditioned diffusion model for multi-class lesion synthesis. Unlike single-class methods, our approach integrates lesion-specific prompts within the diffusion generation process, enhancing diversity and anatomical realism in synthetic data. We validate PromptLesion on a private CCTA dataset and multi-organ tumor segmentation tasks (kidney, liver, pancreas) using public datasets, achieving superior performance compared to baseline methods. Models trained with our prompt-guided synthetic augmentation significantly improve Dice Similarity Coefficient (DSC) scores for both plaque and tumor segmentation. Extensive evaluations and ablation studies confirm the effectiveness of prompt conditioning.