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

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 the transmission and fusion of information between feature maps at different levels and reduce information loss,CAB-U-Net adopts a BiFPN module.Results and Discussions Using the same experimental setup and a dataset comprising 2181 image samples,CAB-U-Net was compared with mainstream networks,including PSPNet,DeepLabv3,U-net,SwinUnet,and TransUnet.CAB-U-Net achieves an IOU of 0.9065,a precision of 0.9332,a recall of 0.9662,and an F1-score of 0.9494,which surpass the results of TransUnet,i.e.,a suboptimal network,by 0.0228,0.0240,and 0.0128,respectively.Although the precision of CAB-U-Net is slightly lower than that of SwinUnet by 0.0012,by comprehensively considering the four abovementioned metrics,CAB-U-Net offers outstanding segmentation performance.The superiority of CAB-U-Net over U-net and TransUnet in terms of segmentation is shown in Figs.6 and 7,respectively.Compared with U-net,our network,which is constructed based on the CFT module,shows higher IOU,precision,recall,and F1-score by 0.0718,0.0605,0.2834,and 0.1768,respectively.This indicates that the proposed CFT module enhances the image feature-extraction capability,thereby improving the ability of the network in capturing calcified plaque lesions.After adding the ASPP module to the CFT module,the network model shows higher IOU,precision,and F1-score by 0.0262,0.0301,and 0.0156,respectively,whereas its recall decreases by 0.0010.This suggests that the ASPP,which utilizes parallel dilated convolutions with multiple sampling rates,extends the receptive field and captures a wider range of contextual information,thereby acquiring multiscale object information.Furthermore,adding the BiFPN module to the CFT module increases the IOU,precision,and F1-score by 0.0299,0.0354,and 0.0176,respectively,and reduces the recall by 0.0028.This indicates that the BiFPN can learn and transmit semantic and lesion-position features at different scales better,thus effectively fusing lesion-feature information at different scales and improving the network s segmentation performance for lesions of different scales.Finally,combining the CFT,ASPP,and BiFPN,CAB-U-Net yields superior overall performance.Incorporating the ASPP and BiFPN effectively enhances the extraction and fusion of multiscale information,enables the model to learn richer and more discriminative features,and improves precision.However,an increase in the model complexity may cause overfitting,thus deteriorating the recall.Based on the comprehensive metric F1-score,incorporating the ASPP and BiFPN strengthens the improvement in the F1-score.Conclusions The CAB-U-Net proposed herein is a convolutional neural network that integrates contextual information for the automatic segmentation of coronary artery calcifications.CAB-U-Net introduces the CFT module,which accurately manages contextual relationships within each position sequence and guides the learning of dynamic attention matrices to enhance feature-extraction capability.The ASPP module is incorporated to utilize dilated convolutions to expand the receptive field and capture contextual information at different scales.Additionally,the BiFPN is adopted to enhance the transmission and fusion of features between feature maps,thereby reducing information loss and achieving more effective feature propagation.Compared with U-net,CAB-U-Net yields higher IOU,precision,recall,and F1-score by 0.1073,0.1037,0.2781,and 0.1972,respectively.Compared with other mainstream segmentation networks,CAB-U-Net exhibits significant advantages.Results of correlation and Bland-Altman analyses show that the area and angle of calcified plaques segmented by CAB-U-Net are consistent with those annotated by experts.Therefore,the proposed CAB-U-net is suitable for the segmentation of calcified plaques in IVOCT images,thus providing objective evidence for the clinical diagnosis of calcified plaques,assisting in the comprehensive assessment of coronary artery calcification lesions,and providing guidance for stent implantation.