Jeong Gyu-Jun, Lee Gaeun, Lee June-Goo, Kang Soo-Jin
Biomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, Korea.
Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Korean Circ J. 2024 Jan;54(1):30-39. doi: 10.4070/kcj.2023.0166. Epub 2023 Oct 16.
Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters.
A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images.
At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average.
The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.
冠状动脉形态的血管内超声（IVUS）评估基于管腔和血管分割。本研究旨在开发一种自动分割算法，并验证测量定量IVUS参数的性能。
总共1063例患者被随机分配，以4:1的比例分为训练集和测试集。获得111例IVUS回撤的独立数据集以评估血管水平的性能。在每个IVUS帧中以0.2毫米的间隔手动标记管腔和外弹力膜（EEM）边界。使用高效U-Net对IVUS图像进行自动分割。
在帧水平上，高效U-Net在管腔分割方面显示出高骰子相似系数（DSC，0.93±0.05）和杰卡德指数（JI，0.87±0.08），在EEM分割方面显示出高DSC（0.97±0.03）和JI（0.94±0.04）。在血管水平上，模型得出的IVUS参数与专家测量的IVUS参数之间存在密切相关性；最小管腔图像面积（r = 0.92）、EEM面积（r = 0.88）、管腔体积（r = 0.99）和斑块体积（r = 0.95）。与专家之间的一致性相比，模型得出的与专家测量的最小管腔面积之间的一致性同样出色。基于模型的20毫米病变节段的管腔和EEM分割需要花费13.2秒，而专家以0.2毫米间隔进行手动分割平均需要187.5分钟。
深度学习模型可以准确、快速地描绘血管几何形状。基于人工智能的方法可能通过在导管实验室中的实时应用来支持临床医生的决策。
