用联邦学习实现多医院冠状动脉斑块自动检测，保护隐私同时提升效率。
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration
- 采用分阶段2D U-Net模型，通过联邦学习在多机构间协作训练。
- 斑块分割Dice系数达0.706，实时识别血管边界并量化斑块负荷。
- 适合医疗数据隐私敏感场景，对介入影像分析有实用价值。
冠状动脉介入治疗中传统IVUS图像解读耗时且依赖医生经验，监管与隐私限制阻碍跨院数据共享。为此，研究提出一种基于联邦学习的并行2D U-Net多阶段分割模型，通过将笛卡尔坐标转为极坐标进行预处理，提升计算效率。模型通过识别并减去外弹性膜（EEM）和管腔区域来分割斑块，在多中心协作下实现隐私保护下的联合建模。最终获得0.706的骰子相似系数（DSC），有效识别斑块并实时检测圆形边界。与领域专家合作增强了斑块负荷的定量解读。未来可结合更先进联邦学习技术并扩展数据集以进一步提升性能。该技术适用于敏感分布式医疗数据环境，有望优化医学影像与干预结果。
原文摘要 · Abstract (English)
The traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention (PCI) is time-intensive and inconsistent, relying heavily on physician expertise. Regulatory restrictions and privacy concerns further hinder data integration across hospital systems, complicating collaborative analysis. To address these challenges, a parallel 2D U-Net model with a multi-stage segmentation architecture has been developed, utilizing federated learning to enable secure data analysis across institutions while preserving privacy. The model segments plaques by identifying and subtracting the External Elastic Membrane (EEM) and lumen areas, with preprocessing converting Cartesian to polar coordinates for improved computational efficiency. Achieving a Dice Similarity Coefficient (DSC) of 0.706, the model effectively identifies plaques and detects circular boundaries in real-time. Collaborative efforts with domain experts enhance plaque burden interpretation through precise quantitative measurements. Future advancements may involve integrating advanced federated learning techniques and expanding datasets to further improve performance and applicability. This adaptable technology holds promise for environments handling sensitive, distributed data, offering potential to optimize outcomes in medical imaging and intervention.
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