基于IVOCT的动脉粥样硬化斑块识别与风险评估

Identification and Risk Assessment of Atherosclerotic Plaques Based on IVOCT

动脉粥样硬化引起的易损斑块破裂已经严重危害到人类的健康,而血管内光学相干断层成像(IVOCT)凭借其高分辨率已经成为识别冠脉易损斑块的主要工具,但图像判读费时费力,通常还依赖于医生的经验。目前已有基于传统机器学习的研究实现了对单帧图像的分类,但这些信息不足以辅助医生确定治疗方案,仍然需要医生二次判读。基于Faster R-CNN(R-CNN,区域卷积神经网络),针对IVOCT图像中易损斑块的特点,在数据增强、预测框(BBox)编码、网络结构等方面进行了改进和优化,实现了对易损斑块的自动识别,并选取易损斑块的病变累积角度、纤维帽厚度、巨噬细胞浸润情况、浅表微钙化情况和血管狭窄程度作为指标,对易损斑块的破裂风险进行多方面评估。在公开数据集CCCV2017 IVOCT中进行训练,测试后取得了较好结果,该方法可推广应用于同类图像。

Objective The rupture of vulnerable plaques caused by atherosclerosis has become one of the most serious threats to human health.Intravascular optical coherence tomography(IVOCT)can accurately identify vulnerable plaque characteristics,such as thin-cap fibroatheroma plaques,owing to its high resolution,and has gradually become the gold standard for the diagnosis of vulnerable plaques.Typically,clinicians must manually mark the location of plaques in an image based on their experience.However,this method is time-consuming and labor-intensive and is susceptible to the subjective assessment of the clinician.Manual interpretation significantly reduces the speed and precision of vulnerable plaque diagnosis.Some studies based on traditional machine learning have been conducted for the detection of vulnerable plaques and have achieved the classification of single-frame images.However,the accuracy of frame-level information is insufficient to assist clinicians in determining treatment strategies.These methods require a second interpretation by clinicians.This study proposes an evaluation algorithm for vulnerable plaque identification in IVOCT images based on an improved Faster R-CNN(regional convolutional neural network)framework.In addition to accurately locating vulnerable plaques,the algorithm can quantitatively assess the risk of plaque rupture,providing diagnostic suggestions to clinicians and assisting in the formulation of treatment plans.The comprehensive nature of this approach is expected to play an important role in improving the efficiency and precision of vulnerable plaque diagnosis.Methods This study is divided into two parts:automatic identification of vulnerable plaques and assessment of vulnerable plaque rupture risk.To identify vulnerable plaques based on the Faster R-CNN,this study proposes an improved strategy for enhanced cyclic shift data,(X,W)encoding BBox,and the introduction of additional semantic segmentation heads according to the characteristics of IVOCT images.The network is generally divided into four parts(feature extraction,region extraction,secondary detection,and A-scan classification),allowing the network to locate vulnerable plaques with higher accuracy.In this study,the angle of accumulation of the lesion,the thickness of the fibrous cap,macrophage infiltration,superficial microcalcification,and vascular stenosis degree of vulnerable plaques are selected as indicators to assess the risk of rupture.The vascular lumen area is used to characterize the degree of vascular stenosis in vulnerable plaques;the smaller the lumen area,the more severe the stenosis.Furthermore,an adaptive threshold method is designed to calculate the thickness of the fibrous cap,which is considered thin when the thickness is less than 65μm.The risk of plaque rupture is indicated by a lesion accumulation angle greater than 90°,and a polar graph is used to measure the lesion accumulation angle.To identify superficial microcalcifications and macrophage infiltration,features are extracted from the images and reclassified.The application of these methods makes our study more comprehensive and accurate.Results and Discussions The proposed method is trained and tested using the public dataset CCCV2017 IVOCT.This study presents the results of the ablation experiment for Faster R-CNN(Table 2).The improved network performs well in positioning vulnerable plaques,with mAP50 increasing to 0.744 and the Dice value increasing to 0.905.Compared with weakly supervised detection(WSD)and salient-region-based convolutional neural network(SRCNN)methods,the method proposed in this study significantly improves the recall and Dice values(Table 3).The intersection of union(IOU)value of the lumen area is O.9445,and the prediction result is consistent with actual result of the lumen area[Fig.7(c)].The root mean square error Rmse and the goodness of fit R are used to verify the feasibility of the calculation of the thickness of the fiber cap,and the test results are 1.17 pixel and O0.62.respectively.After positioning the region accurately,the cumulative angle of the lesion is also accurately assessed[Fig.7(d)].To evaluate the performance of the model in predicting superficial plaque microcalcifications and macrophage infiltration,a comprehensive analysis is performed using a confusion matrix[Figs.7(e)and(f)].These results demonstrate that the proposed method achieves satisfactory results for multiple evaluation metrics and provides a reliable solution for the identification of vulnerable plaques andruptureriskassessment.ConclusionsIn this study,the cyclic shift,(X,W),and encoding BBox are added,and additional semantic segmentation heads are introduced to the Faster R-CNN network to improve the detection performance for vulnerable plaques.Compared to the initial network and adding only a single change,the method proposed in this study significantly improves the mAP50 and Dice values of the network.Compared with WSD and SRCNN,our method also achieves significant improvements in the recall rate and Dice value.Furthermore,to obtain accurate location results for the vulnerable plaque region,the angle of the plaque region is used to measure the angle of accumulation of the lesion,and the cumulative pixels of the fiber cap are used to calculate the thickness of the fiber cap.Deep neural network features combined with gradient direction histogram features are used to analyze macrophage infiltration and superficial microcalcification,and the vascular stenosis degree is evaluated in the lesion lumen region.Multiple single-evaluation results are used to measure the risk of rupture of vulnerable plaques.The comprehensive method proposed in this paper achieves a significant breakthrough in vulnerable plaque detection and provides more comprehensive and reliable data support for clinical diagnosis in terms ofrupture risk assessment.