基于卷积神经网络的IVOCT冠状动脉钙化斑块分割方法  

IVOCT Coronary Artery Calcification Plaque Segmentation Based on Convolutional Neural Network

作　　者：夏巍 韩婷婷[1,2] 陶魁园 王为 高静[1,2] Xia Wei;Han Tingting;Tao Kuiyuan;Wang Wei;Gao Jing(School of Electronic and Communication Engineering,Tianjin Normal University,Tianjin 300387,China;Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission,Tianjin 300387,China;State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,Jiangsu,China)

机构地区：[1]天津师范大学电子与通信工程学院,天津300387 [2]天津市无线移动通信与无线电能传输重点实验室,天津300387 [3]南京航空航天大学机械结构力学与控制国家重点实验室,江苏南京210016

出　　处：《中国激光》2024年第18期225-234,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(11404240,62001328);天津市科技计划项目(20JCYBJC00300)。

摘　　要：冠状动脉钙化斑块检测在评估疾病和介入手术效果中至关重要。血管内光学相干断层扫描(IVOCT)作为一种强大的影像技术,常用于评估冠状动脉疾病(CAD)和规划经皮冠状动脉介入治疗(PCI),其分辨率比冠状动脉造影、计算机断层扫描或磁共振成像高出数个数量级。在IVOCT影像采集过程中,每次回撤扫描会生成300~500张图像,医护人员在短时间内完成冠状动脉钙化斑块的定量分析是一项巨大的挑战。提出一种上下文信息融合的卷积神经网络(CAB-U-Net),用于IVOCT影像中冠状动脉钙化斑块自动分割。基于CAB-U-Net设计一种动态注意力机制和位置编码结合的CFT(context fusion transformer)模块,用于增强网络特征提取能力。CAB-U-Net引入ASPP(atrous spatial pyramid pooling)和BiFPN(Bi-directional feature pyramid network)模块,加强多尺度特征图之间的信息传递和融合。利用CAB-U-Net在48位临床患者的IVOCT图像上进行训练和测试,结果显示交并比(IOU)为0.9065,精确度为0.9332,召回率为0.9662,F1-score为0.9494。CAB-U-Net自动分割的钙化斑块的角度和面积,与专家标注结果的相关性系数分别达到0.9535和0.9894。Bland-Altman分析结果进一步证实该方法和专家手动标注结果在分割钙化斑块方面具有良好的一致性。所提出的方法对自动评估冠状动脉钙化病变和术中支架部署规划具有较高的价值。Objective Intravascular optical coherence tomography(IVOCT)is an advanced imaging technique that enables clear visualization of the contours and morphology of calcified coronary artery plaques,thus aiding in the diagnosis of coronary artery disease and the evaluation of percutaneous coronary intervention(PCI).However,each pullback scan generates 300‒500 images.During PCI procedures,comprehensively analyzing such a large volume of images is challenging,and inconsistencies in annotations may exist between observers and the same observer at different times.Hence,a fast,accurate,and efficient approach for the automatic segmentation and evaluation of calcified plaques during surgery must be adopted.Therefore,this study proposes a convolutional neural network,named CAB-U-Net(context fusion transformer-atrous spatial pyramid pooling-bidirectional feature pyramid network),based on IVOCT images for the automatic segmentation of coronary artery calcified plaques via the integration of contextual information.Methods The proposed CAB-U-Net network for coronary artery calcified plaque segmentation is an improvement of the U-Net architecture.The network primarily comprises Conv2D Block,context fusion transformer(CFT),atrous spatial pyramid pooling(ASPP),and Bi-directional feature pyramid network(BiFPN)modules.The Conv2D Block comprises convolutional,batch normalization,and Sigmoid linear unit(SiLU)activation layers.It aims to enhance feature extraction,accelerate neural-network training,and improve model generalization.The CFT module accurately manages the contextual relationships within sequences via position encoding.It utilizes contextual information between input keys to guide the learning of dynamic attention matrices,thereby enhancing the feature-extraction capability.Additionally,the ASPP introduced in CAB-U-Net enlarges the receptive field through dilated convolutions to capture contextual information at different scales without increasing the network parameters and computational complexity.Furthermore,to strengthen

关 键 词：深度学习 生物医学 血管内光学相干断层扫描 冠状动脉钙化斑块分割 卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]