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

作　　者：韩泽君 林兴康 裘耀阳 张晓[1] 高磊 李勤[1] Han Zejun;Lin Xingkang;Qiu Yaoyang;Zhang Xiao;Gao Lei;Li Qin(School of Medical Technology,Beijing Institute of Technology,Beijing 100081,China;The Sicth Medical Center of PLA General Hospital,Beijing 100048,China)

机构地区：[1]北京理工大学医学技术学院,北京100081 [2]中国人民解放军总医院第六医学中心,北京100048

出　　处：《中国激光》2024年第9期227-237,共11页Chinese Journal of Lasers

基　　金：国家自然科学基金(61975017);首都卫生发展科研专项(首发2024-2-5072)。

摘　　要：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

关 键 词：医用光学 动脉粥样硬化 血管内光学相干断层成像 易损斑块 自动识别 风险评估 

分 类 号：O436[机械工程—光学工程]