Liu Pengfei, Lu Zang, Hou Wenqing, Kadier Kaisaierjiang, Cui Chunying, Mu Zhengyang, Ainiwaer Aikeliyaer, Peng Xinliang, Wufu Gulinuer, Ma Yitong, Dai Jianguo, Ma Xiang
Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
College of Information Science and Technology, Shihezi University, Shihezi 832003, Xinjiang, China.
iScience. 2025 Mar 6;28(4):112169. doi: 10.1016/j.isci.2025.112169. eCollection 2025 Apr 18.
The process of manually characterizing and quantifying coronary artery plaque tissue in intravascular optical coherence tomography (IVOCT) images is both time-consuming and subjective. We have developed a deep learning-based semantic segmentation model (EDA-UNet) designed specifically for characterizing and quantifying coronary artery plaque tissue in IVOCT images. IVOCT images from two centers were utilized as the internal dataset for model training and internal testing. Images from another independent center employing IVOCT were used for external testing. The Dice coefficients for fibrous plaque, calcified plaque, and lipid plaque in external tests were 0.8282, 0.7408, and 0.7052 respectively. The model demonstrated strong correlation and consistency with the ground truth in the quantitative analysis of calcification scores and the identification of thin-cap fibroatheroma (TCFA). The median duration for each callback analysis was 18 s. EDA-UNet model serves as an efficient and accurate technological tool for plaque characterization and quantification.
在血管内光学相干断层扫描（IVOCT）图像中手动表征和量化冠状动脉斑块组织的过程既耗时又主观。我们开发了一种基于深度学习的语义分割模型（EDA-UNet），专门用于表征和量化IVOCT图像中的冠状动脉斑块组织。来自两个中心的IVOCT图像被用作模型训练和内部测试的内部数据集。来自另一个采用IVOCT的独立中心的图像用于外部测试。外部测试中纤维斑块、钙化斑块和脂质斑块的Dice系数分别为0.8282、0.7408和0.7052。该模型在钙化评分的定量分析和薄帽纤维粥样瘤（TCFA）的识别中与地面真值表现出很强的相关性和一致性。每次回调分析的中位持续时间为18秒。EDA-UNet模型是一种用于斑块表征和量化的高效、准确的技术工具。
