基于机器学习的冠状动脉血管内OCT影像分割文献综述

 2022-11-03 21:49:37

文献综述（或调研报告）：

血管内图像分割及三维重建综述

近年来，对医学图像进行血管提取一直是医疗图像领域的研究热点。Shiffman[36]等人采用人工识别和神经网络相结合的方法，分割出 CT 图像中的血管结构。该方法首先分割出图像中的所有结构，然后对血管结构进行标记，最后再分割出标记的血管结构。Park[37]等为了提高图像的质量利用中值滤波对图像进行预处理，最后结合区域生长算法实现血管的分割。孙正[38]等人分割了冠状动脉造影图像的血管，在分割过程中为了完成拓扑变化，将节点进行拆分，并加入了能够使轮廓曲线从血管内部向血管外部演化的能量控制因子。Wang Z[39]等人提出了一种用于冠脉内 OCT 管腔和钙化斑块自动分割的方法。在 OCT 图像矩形视图中，利用动态规划方法实现了对管腔的分割，钙化斑块使用边缘检测进行局部化，通过活动轮廓模型精细追踪。Stavros T[40]等人设计并提出了一种结合纹理和边缘信息的鲁棒性自动分割技术，用于血管内 OCT 管腔边界的提取。Chatzizisis[41]等人研究了冠脉内 OCT 图像自动分割算法。该算法首先应用灰度变换、中值滤波等对 OCT 图像进行预处理，将图像转换为二值图像，然后应用形态滤波器提取轮廓点，最后采用低通 Haar 滤波器对轮廓进行光滑处理。Celi S[42]等人提出了一个新的框架来对冠状动脉图像进行分割和量化，以确定狭窄等级和斑块类型。当管腔边界封闭时，首先对图像进行二值化，然后采用形态滤波。当边界开放时，用 Otsu 自动阈值法产生一个二值图像，用形态学闭运算填充动脉壁孔洞，通过插值连接径向线与边界的交叉点得到分割结果。Kenji等人研究了包括一些不规则几何形状的血管内轮廓的提取及自动提示与修复。该论文利用相邻图像的血管内轮廓的面积作为一个评价指标判定内轮廓几何形状是否异常。对于无法获取的轮廓部分，他们尝试采用限定圆心，半径渐变的圆弧拟合予以弥补，但需要人工审核修正[43]。Tung等人采用期望最大化和图割相结合的方法提取冠状动脉的血管壁内轮廓，该算法可以去除导丝带来的伪影，但时间开销较大[44]。Serhan等人提出基于阈值滤波和 Catmull—Rom样条曲线迭代拟合的冠状动脉血管壁内轮廓提取算法[45]。Kauffmann等人采用形态学方法和主动轮廓模型确定冠状动脉的内轮廓，同时对获取轮廓进行椭圆拟合来判定当前冠脉截面图像是否正常，并根据极坐标变换后图像的梯度信息确定支架位置，计算支架与血管壁之间的距离，最终采用Thin—Plate Spline(TPS)拟合出内膜增生厚度分布图，并对病人冠脉内膜覆盖状况进行评估[46]。舒鹏、孙延奎等人提出了一种基于帧间相关性和极坐标变换的冠状动脉血管壁内轮廓提取算法[47]：首先采用帧间相关性去除鞘管的影响；然后采用射线发射法估计血管中心，并将其作为极点进行极坐标变换；最后提取极坐标变换后图像的上边缘并进行极坐标逆变换得到最终的结果。

在图像处理领域，图像边缘检测问题可以看成是边缘点与非边缘点的分类问题，因此可以用机器学习中的分类思想进行研究。因此有学者将机器学习分类算法理论引入到图像的边缘问题的研究中来。2004 年，Fowlkes 等人将像素点的颜色、纹理、局部亮度、梯度信息作为特征，训练出一个分类器来估计边缘的概率，利用中层感知信息改进了图像边缘检测的结果。2005年，一种基于 PBT（Probabilistic Boosting Tree）分类的 BEL 边缘检测算法[48]被 Tu 等人提出。郭小亚，tang等人基于最小二乘支持向量机（LS-SVM）的分割方法对冠状动脉内光学相干断层扫描（OCT）图像识别斑块成分的边界并量化纤维帽厚度[49]。

血管图像的三维建模因其在医学诊疗的重要意义引起了许多学者的研究。文献[50]中对从各帧 IV-OCT 图像序列中提取的血管壁内膜轮廓直接构造血管内壁表面的方法进行了初步探索，但是该方法重构的血管表面不太光滑。Athanasio等[51]提出了一种采用临床采集的 FD-OCT 图像序列对冠脉血管进行三维重建的方法，该方法将来自双平面造影的血管曲率信息和 FD-OCT 检测到的管腔和管壁信息以及回撤产生的所有边界获得了 3D 动脉几何结构结合起来，获得冠脉血管的三维重建。Ellwein 等[52]提出将CT 与 OCT 图像进行融合，对感兴趣的血管段进行重建，具体方法是：利用 CT 图像获得冠状动脉血管的空间结构，并采用最小弯曲能量 Dijkstra 算法求得对应的最短路径即为导丝路经，将 OCT 获得的血管轮廓图进行定位获得对应的三维血管模型。Sihan 等[53]尝试采用基于 K 近邻的支架检测的方法，完成对 OCT 图像序列的批操作，但是针对聚类算法和伪影的问题还需进一步研究。Giovanni 等[54]采用一种将 IV-OCT 图像序列进行全自动的三维可视化方案，可实现 3D OCT 的在线应用，可辅助经皮冠状动脉诊断治疗以及治疗方案的改进。这种方法精确、快速，适用于冠状动脉介入治疗的在线应用，但是所得3D 图像的分辨率较低，且容易形成运动伪影[55]。屠子美等[56]借助 Amira 医学图像处理工具，完成三维可视化，实现三维动脉成像。但是在伪影的去除方面存在不足，整个重建过程复杂繁琐，而且采用 Amira 软件进行三维成像的效果欠佳。

国内外多个研究小组基于医学图像模型进行数值模拟计算的相关研究。2001年，Huang基于20个冠状动脉斑块样本（10个斑块破裂，10个稳定斑块）建立二维模型计算分析，认为应力最大值与斑块钙化比例无关，但是与斑块脂质部分的比例成正比[57]。Holzapfel认为一个多层的各向异性的Ｈ维模型来得到血管中不同材料部分的力学响应[58]。Huang建立多个颈动脉模型定量计算了轴向与周向的血管收缩系数来得到血管初始计算模型状态[59]。2015年，Wang基于IVUS建立了多个三维流固耦合模型，对斑块増长的相关性进行研究，得到了壁厚与血管内壁应为两者的结合作为斑块増长的最佳预测因素[61]。Tang等人从2009年起基于医学图像数据建立了系统的三维流固耦合数值模型，指出了冠脉循环弯曲和各向异性材料参数对应力计算的影响,同时有效地模拟血管及斑块受力情况，用以研究流体和斑块的交互作用，其研究表明在脉动流压力下斑块中的大幅应力应变循环变化可能会导致斑块疲劳，从而进一步发生结构破坏[26,27,60,62-64]。Bank､Versluisa､Stehbens等还尝试用结构的疲劳来解释斑块的破裂过程，他们首先提出斑块疲劳破坏的概念，随之发展出疲劳裂纹扩展模型Ｗ评估动脉的解剖结构、血压对斑块疲劳破坏过程中裂纹的初始位置、断裂路径及断裂速率等动力学状态的影响[65-67]。陈槐卿等通过兔高脂血症动脉粥样硬化模型探讨了血流动力学因素在动脉粥样硬化形成过程中的变化规律[68]。苏海军等从弹性力学的稳定性角度出发，利用有矩理论建立了动脉粥样硬化斑块纤维帽运动的静力学和动力学模型，合理的讨论了早期斑块的稳定性[69]。

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