颈动脉MR图像分割和三维重建在斑块定位中的应用

背景：医学图像的三维重建在医疗诊断、实验分析中起着越来越重要的作用,它是一项复杂的任务,其中目标图像的分割是首要且重要的一步。 目的：探索对颈动脉MR图像的图像分割及三维重建方法,并探讨三维模型在颈动脉斑块定位中的应用。 方法：选择3D TOF序列图像对其进行基于最大熵原理的阈值分割,并与普通方法的结果做比较;进一步用数学形态学分割方法提取出颈动脉;进行三维重建,利用三维模型进行斑块的初步定位。 结果与结论：基于最大熵原理的阈值分割适于对颈动脉3D TOF序列图像的分割,用数学形态学分割方法进行后续分割可得到目标图像。三维重建后的模型对于斑块定位有辅助作用。     

颈动脉分叉处血管粥样硬化斑块的体内应力分析

目的探究颈动脉分叉处血管斑块的体内应力分布,为颈动脉分叉处血管斑块破裂行为的研究和诊断治疗方案的设计提供力学机理参考。方法基于人体颈动脉分叉血管的平均几何参数,建立三维颈动脉分叉血管及其斑块的几何模型,通过"热-结构"耦合重建颈动脉分叉血管及其斑块的残余应力,并计算血压和血流分别作用下颈动脉分叉处血管斑块的体内应力。结果斑块的肩部同时存在着最大主应力和弹性剪切应力的应力集中。斑块肩部的弹性剪切应力随着狭窄率增大或血压升高均增加。斑块上游区域的流体壁面切应力明显高于斑块下游区域,斑块下游区域的振荡剪切指数则显著大于上游区域。且斑块的弹性剪切应力和流体壁面切应力大小随着狭窄率的变化呈现出不同的变化规律。结论斑块从内部中心位置到壁面肩部承受着非均匀的应力分布,血管严重狭窄时"内压外拉"的受力状态更容易导致斑块破裂。随着血压的变化,斑块结构应力的周期性变化可能使斑块产生结构疲劳,增加破裂风险。斑块上下游区域流体动力学参数的差异可能是斑块上下游组分、易损程度等性质不同的原因之一。     

颈动脉斑块的流固耦合数值模拟

目的 提出一种基于真实颈动脉狭窄患者的双向流固耦合计算方法,用于分析不同类型颈动脉斑块在病变处的血流动力学参数以及斑块自身的变形和应力变化。方法 对颈动脉中度狭窄病人CT血管造影数据进行3D建模,分离出颈动脉血管壁模型和斑块模型,并进行瞬态流固耦合计算。模拟颈动脉粥样硬化的早期到形成斑块,将斑块类型分为增厚斑块、脂质斑块、混合斑块和钙化斑块,其中增厚斑块视为无斑块的情况,代表血管内膜-中膜的增厚,并对不同斑块类型的狭窄颈动脉进行对比分析。结果 不同类型斑块对整体血流流动影响不大,但是脂质斑块病变处的壁面剪切应力低于其余斑块;以增厚斑块作为对照,斑块出现会抑制动脉的扩张,其中脂质斑块最为明显;钙化斑块有最高的平均结构应力,相反脂质斑块平均结构应力则是最低。结论 本文所提出的计算方法能够同时对流体和固体区域进行分析。研究结果有助于更好理解不同斑块类型对于颈动脉病变处的影响。     

基于Transformer的血管内超声图像分割方法

目的 提出一种基于Transformer的血管内超声图像分割方法，以解决冠状动脉钙化病变血管内超声图像显影不完全导致的分割管腔、外弹力膜和钙化斑块精度不高的问题。方法 采用深度学习方法，在UNet结构的基础上用多分辨率卷积层提取不同大小类别特征，在特征编码模块与特征解码模块之间使用Transformer联系上下文信息，同时分割管腔、外弹力膜和钙化斑块。最后以34个40 MHz血管内超声序列得到的720张训练集和240张测试集为例对上述方法进行训练和测试。结果 外弹力膜分割杰卡德系数（Jaccard index,JI）为0.92，豪斯多夫距离（Hausdorff distance,HD）为0.84 mm；管腔分割JI为0.85, HD为1.44 mm；钙化斑块分割JI为0.67, HD为0.68 mm。结论 该方法能够提升血管内超声图像的分割精度，并且在钙化病变血管显影不完全时能够保持分割效果。     

能谱CT成像与颈部血管超声评价颈动脉狭窄的对比研究

目的 探讨能谱CT成像与颈部血管超声在颈动脉狭窄评价中的应用价值。方法 纳入2016年6月—2021年6月河南省驻马店市中心医院数字减影血管造影（DSA）确诊为颈动脉狭窄的缺血性脑卒中（IS）患者289例,其中男159例、女130例,年龄43~82（61.7±8.0）岁。患者均行能谱CT和颈部血管超声检查,以DSA诊断结果为金标准,比较能谱CT及颈部血管超声诊断颈动脉狭窄程度的一致性;比较2种检查方法对不同程度颈动脉狭窄的诊断效能;比较2种检查方法对颈总动脉易损斑块的检出率。结果 以DSA诊断结果为金标准,能谱CT及超声检测颈动脉狭窄程度Kappa值分别为0.844、0.882,均具有较好的一致性;除了颈部血管超声诊断颈动脉轻度狭窄的灵敏度较低、能谱CT诊断颈动脉轻度狭窄的阳性预测值较低外,能谱CT及颈部血管超声对不同程度颈动脉狭窄的诊断均具有较高的准确率、灵敏度、特异度、阳性预测值及阴性预测值。在颈总动脉上,DSA共检出易损斑块1 672个;颈部血管超声检出876个,其中软斑块384个、表面不规则斑块80个、溃疡斑412个,易损斑块检出率为52.39%（876/1 672）;能谱CT检出367个,均为软斑块,易损斑块检出率为21.95%（367/1 672）。血管超声易损斑块检出率高于能谱CT,差异有统计学意义（χ²=509.00,P<0.001）。结论 能谱CT成像与颈部血管超声均能准确判断颈动脉狭窄程度,超声可能在颈总动脉易损斑块评估中更具价值,临床应用时可将两种方式联合使用。     

基于超声体模的颈动脉血流介导血管扩张斑块高度测量方法比较

通过设计实验对超声体模的血管扩张功能(FMD)斑块高度进行了测量,对各种测量方法做出了定性与定量分析和比较,从而确定最佳的评估方法及软件,以用于颈动脉粥样硬化临床早期诊断和治疗。以颈动脉超声体模的实际尺寸作为金标准,对FMD斑块高度进行了测量,利用超声诊断仪专用软件和自行开发的分割软件,分别使用3种不同的测量方法：横向直接测量、横向间接测量和纵向直接测量,获取斑块的高度,并进行了比较和分析。结果表明,在横向间接测量方法中,超声诊断仪专用软件比自行开发的分割软件更为准确;而在横向直接测量和纵向直接测量中,自行开发的分割软件比超声诊断仪专用软件更为准确。自行开发的分割软件与超声诊断仪专用软件的测量结果在统计学上存在显著性差异(P<0.05),但两者相差的绝对值并不大（对应0.30、0.45、0.60 cm等 3种斑块高度,小于0.02 cm）。总体来说,超声诊断仪专用软件和自行开发的分割软件测量结果都接近于金标准,但自行开发的分割软件的精确性优于超声诊断仪专用软件     

颈动脉易损斑块的生物力学机制和破裂风险评价指标

颈动脉粥样硬化斑块的破裂和继发血栓形成是导致缺血性脑中风的最主要原因。颈动脉斑块局部的生物力学状况及形态、成分和生物学活性均与颈动脉斑块的破裂风险密切相关。综述和分析颈动脉狭窄斑块生物力学的研究方法、颈动脉狭窄易损斑块破裂风险及临床治疗决策、颈动脉易损斑块的动物模型及实验、颈动脉斑块成分和生物学活性等方面的研究进展和存在问题,并就有关颈动脉易损斑块的生物力学机制和破裂风险评价指标的研究前景进行展望,希望为确定合理的颈动脉狭窄治疗适应证提供必要的理论依据。     

颈动脉斑块的超声仿真及系统实现

根据对颈动脉斑块几何形状、组织特征及声学特性的分析,提出基于FIELD II颈动脉斑块血管的超声仿真方法,并利用MATLAB中的图形用户界面设计开发超声仿真系统。以基于元球的隐式曲面造型技术对斑块血管进行三维几何建模,以空间位置伽马随机分布的散射点对其进行组织建模,超声仿真并建立仿真系统。对系统仿真的超声图像进行统计分析,与模型预设值及临床B超图像进行比较,结果充分验证了本文仿真方法及仿真系统的有效性。     

颈动脉粥样硬化斑块影像学诊断方法的研究进展

颈动脉粥样硬化伴斑块（CP）脱落可诱发血栓形成,导致脑血管疾病发生。近年来,对于CP的研究主要集中在易损斑块的性质及数量。因此,有效评价斑块结构及稳定性,对于选择治疗时机和治疗方案有重要临床意义。从超声、超声造影成像、超声实时组织弹性成像、血管内超声、数字减影血管造影、CT血管造影、磁共振血管成像及光学相干断层成像等方面对斑块的诊断方法进行综述,并对颈动脉斑块影像学检查进行评价。     

颈动脉粥样硬化易损斑块判定的相关影像学研究

颈动脉粥样硬化斑块具有不规则或是溃疡表面,是斑块易于破损的主要因素。影像学检查可不同程度地对颈动脉粥样硬化斑块进行评测,为临床疾病诊断和治疗提供可靠、安全、经济的客观依据。文章对颈动脉粥样硬化易损斑块位置、分型及判定的相关影像学检查技术如颈动脉超声、血管内超声、计算机断层血管成像、数字减影血管造影、高分辨率MRI等研究进展进行阐述,详细分析二维颈动脉超声、颈部超微血管成像、颈部超声造影、颈部超声弹性成像、颈部三维超声成像等技术进展,为临床精准诊疗提供更合理的参考。     

Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound Images

In this paper, we present automatic image segmentation and classification technique for carotid artery ultrasound images based on active contour approach. For early detection of the plaque in carotid artery to avoid serious brain strokes, active contour-based techniques have been applied successfully to segment out the carotid artery ultrasound images. Further, ultrasound images might be affected due to rotation, scaling, or translational factors during acquisition process. Keeping in view these facts, image alignment is used as a preprocessing step to align the carotid artery ultrasound images. In our experimental study, we exploit intima–media thickness (IMT) measurement to detect the presence of plaque in the artery. Support vector machine (SVM) classification is employed using these segmented images to distinguish the normal and diseased artery images. IMT measurement is used to form the feature vector. Our proposed approach segments the carotid artery images in an automatic way and further classifies them using SVM. Experimental results show the learning capability of SVM classifier and validate the usefulness of our proposed approach. Further, the proposed approach needs minimum interaction from a user for an early detection of plaque in carotid artery. Regarding the usefulness of the proposed approach in healthcare, it can be effectively used in remote areas as a preliminary clinical step even in the absence of highly skilled radiologists.     

A review of ultrasound common carotid artery image and video segmentation techniques

The determination of the wall thickness [intima–media thickness (IMT)], the delineation of the atherosclerotic carotid plaque, the measurement of the diameter in the common carotid artery (CCA), as well as the grading of its stenosis are important for the evaluation of the atherosclerosis disease. All these measurements are also considered to be significant markers for the clinical evaluation of the risk of stroke. A number of CCA segmentation techniques have been proposed in the last few years either for the segmentation of the intima–media complex (IMC), the lumen of the CCA, or for the atherosclerotic carotid plaque from ultrasound images or videos of the CCA. The present review study proposes and discusses the methods and systems introduced so far in the literature for performing automated or semi-automated segmentation in ultrasound images or videos of the CCA. These are based on edge detection, active contours, level sets, dynamic programming, local statistics, Hough transform, statistical modeling, neural networks, and an integration of the above methods. Furthermore, the performance of these systems is evaluated and discussed based on various evaluation metrics. We finally propose the best performing method that can be used for the segmentation of the IMC and the atherosclerotic carotid plaque in ultrasound images and videos. We end the present review study with a discussion of the different image and video CCA segmentation techniques, future perspectives, and further extension of these techniques to ultrasound video segmentation and wall tracking of the CCA. Future work on the segmentation of the CCA will be focused on the development of integrated segmentation systems for the complete segmentation of the CCA as well as the segmentation and motion analysis of the plaque and or the IMC from ultrasound video sequences of the CCA. These systems will improve the evaluation, follow up, and treatment of patients affected by advanced atherosclerosis disease conditions.     

基于深度学习的颈动脉超声图像内中膜厚度测量

目的 颈动脉血管内中膜厚度(IMT)是衡量动脉粥样硬化程度的重要标准.一般采用人工标定进行测量,该过程耗时且繁琐,由此提出一种总体性能较好的全自动分割(AS)算法.方法 该算法首先利用卷积神经网络(CNN)识别出颈动脉血管远端,进而提取包含颈动脉内膜、中膜部分的感兴趣区域(ROI).采用基于堆栈式自编码器(SAE)构造的模式分类器将ROI中的像素进行分类.最后利用分类区域的面积信息和位置信息对分类结果进行甄别,运用曲线拟合提取边界完成测量任务.结果 针对本研究所用图像库中的84幅颈动脉超声图像进行实验,金标准(GT)由两名专家4次测量的平均值产生,其与AS之间的绝对误差和标准差为(13.3±20.5) μm,协方差系数为0.990 7.结论 实验结果表明,此算法总体性能较好,能够实现超声颈动脉血管内中膜全自动、快速、准确分割,从而满足临床需要.     

Automatic navigation path generation based on two-phase adaptive region-growing algorithm for virtual angioscopy

In this paper, we propose a fast and automated navigation path generation algorithm to visualize inside of carotid artery using MR angiography images. The carotid artery is one of the body regions not accessible by real optical probe but can be visualized with virtual endoscopy. By applying two-phase adaptive region-growing algorithm, the carotid artery segmentation is started at the initial seed, which is located on the initially thresholded binary image. This segmentation algorithm automatically detects the branch position with stack feature. Combining with a priori knowledge of anatomic structure of carotid artery, the detected branch position is used to separate the carotid artery into internal carotid artery and external carotid artery. A fly-through path is determined to automatically move the virtual camera based on the intersecting coordinates of two bisectors on the circumscribed quadrangle of segmented carotid artery. In consideration of the interactive rendering speed and the usability of standard graphic hardware, endoscopic view of carotid artery is generated by using surface rendering algorithm with perspective projection method. In addition, the endoscopic view is provided with ray casting algorithm for off-line navigation of carotid artery. Experiments have been conducted on both mathematical phantom and clinical data sets. This algorithm is more effective than key-framing and topological thinning method in terms of automated features and computing time. This algorithm is also applicable to generate the centerline of renal artery, coronary artery, and airway tree which has tree-like cylinder shape of organ structures in the medical imagery.     

基于卷积神经网络检测颈动脉斑块

提出一种基于卷积神经网络的自动检测超声图像颈动脉斑块的方法。通过超分辨生成对抗网络提高超声图像质量,并采用高斯混合模型算法结合先验知识自动提取感兴趣区域;最后采用卷积神经网络实现颈动脉有无斑块的自动检测。使用上海市奉贤区中心医院提供的数据集,自动检测颈动脉是否有斑块,模型准确度、敏感度、特异度分别达到94.11%、96.30%、91.67%。实验证明基于卷积神经网络检测颈动脉斑块结果和真实值有很高的一致性,且鲁棒性好。     

Quantification of carotid vessel wall and plaque thickness change using 3D ultrasound images

Quantitative measurements of carotid plaque burden progression or regression are important in monitoring patients and in evaluation of new treatment options. 3D ultrasound (US) has been used to monitor the progression or regression of carotid artery plaques. This paper reports on the development and application of a method used to analyze changes in carotid plaque morphology from 3D US. The technique used is evaluated using manual segmentations of the arterial wall and lumen from 3D US images acquired in two imaging sessions. To reduce the effect of segmentation variability, segmentation was performed five times each for the wall and lumen. The mean wall and lumen surfaces, computed from this set of five segmentations, were matched on a point-by-point basis, and the distance between each pair of corresponding points served as an estimate of the combined thickness of the plaque, intima, and media (vessel-wall-plus-plaque thickness or VWT). The VWT maps associated with the first and the second US images were compared and the differences of VWT were obtained at each vertex. The 3D VWT and VWT-Change maps may provide important information for evaluating the location of plaque progression in relation to the localized disturbances of flow pattern, such as oscillatory shear, and regression in response to medical treatments.     

Snakes based segmentation of the common carotid artery intima media

Ultrasound measurements of the human carotid artery walls are conventionally obtained by manually tracing interfaces between tissue layers. In this study we present a snakes segmentation technique for detecting the intima-media layer of the far wall of the common carotid artery (CCA) in longitudinal ultrasound images, by applying snakes, after normalization, speckle reduction, and normalization and speckle reduction. The proposed technique utilizes an improved snake initialization method, and an improved validation of the segmentation method. We have tested and clinically validated the segmentation technique on 100 longitudinal ultrasound images of the carotid artery based on manual measurements by two vascular experts, and a set of different evaluation criteria based on statistical measures and univariate statistical analysis. The results showed that there was no significant difference between all the snakes segmentation measurements and the manual measurements. For the normalized despeckled images, better snakes segmentation results with an intra-observer error of 0.08, a coefficient of variation of 12.5%, best Bland–Altman plot with smaller differences between experts (0.01, 0.09 for Expert1 and Expert 2, respectively), and a Hausdorff distance of 5.2, were obtained. Therefore, the pre-processing of ultrasound images of the carotid artery with normalization and speckle reduction, followed by the snakes segmentation algorithm can be used successfully in the measurement of IMT complementing the manual measurements. The present results are an expansion of data published earlier as an extended abstract in IFMBE Proceedings (Loizou et al. IEEE Int X Mediterr Conf Medicon Med Biol Eng POS-03 499:1–4, 2004).     

Semi-automated carotid lumen segmentation in computed tomography angiography images

Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of this study was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and more importantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define the severity of stenosis and present a three-dimensional model of the carotid for visual inspection. In vivo contrast CTA images of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-step segmentation algorithm. This method uses graph cut followed by watershed and Hessian based shortest path method in order to extract lumen boundary correctly without being corrupted in the presence of surrounding tissues. Quantitative measurements of the proposed method were compared with those of manual delineation by independent board-certified radiologists. The results were quantitatively evaluated using spatial overlap surface distance indices. A slightly strong match was shown in terms of dice similarity coefficient (DSC) = 0.87_x005f
0.08; mean surface distance (Dmsd) = 0.32
0.32; root mean squared surface distance (Drmssd) = 0.49
0.54 and maximum surface distance (Dmax) = 2.14
2.08 between manual and automated segmentation of common, internal and external carotid arteries, carotid bifurcation and stenotic artery, respectively. Quantitative measurements showed that the proposed method has high potential to segment the carotid lumen and is robust to the changes of the lumen diameter and the shape of the stenosis area at the bifurcation site. The proposed method for CTA images provides a fast and reliable tool to quantify the severity of carotid artery stenosis.