当前位置：
X-MOL 学术
›
Int. J. Numer. Method. Biomed. Eng.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Supervised machine learning for coronary artery lumen segmentation in intravascular ultrasound images.
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2020-05-19 , DOI: 10.1002/cnm.3348 Hengfei Cui 1, 2 , Yong Xia 1, 2 , Yanning Zhang 1
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2020-05-19 , DOI: 10.1002/cnm.3348 Hengfei Cui 1, 2 , Yong Xia 1, 2 , Yanning Zhang 1
Affiliation
|
Intravascular ultrasound (IVUS) has been widely used to capture cross sectional lumen frames of inner wall of coronary arteries. This kind of medical imaging modalities is capable of providing detailed and significant information of lumen contour shape, which is very important for clinical diagnosis and analysis of cardiovascular diseases. Numerous learning based techniques have recently become very popular for coronary artery segmentation due to their impressive results. In this work, a supervised machine learning method for coronary artery lumen segmentation with high accuracy and minimal user interaction is designed. The fully discriminative lumen segmentation method jointly learning a classifier the weak learners rely on and the features of the classifier is developed. Additionally, the theoretical supports of the Gradient Boosting framework used in this work and its quadratic approximation are presented. The proposed algorithm is tested on the public datasets of boundary detection of lumen in IVUS challenge held in MICCAI 2011 and achieves a higher average Jaccard similarity of 96.8% and a lower mean error distance of 0.55 (in Cartesian coordinates), which shows higher accuracy compared to the existing learning based methods. Moreover, three real patient IVUS datasets are used to evaluate the performance of the proposed coronary artery lumen segmentation algorithm, which is shown to achieve lower percent error of lumen area of 1.861% ± 0.965%, 1.968% ± 0.864%, and 1.671% ± 0.584%, respectively, compared to the manually measured lumen area (ground truth). The proposed lumen segmentation method is found to be superior to the latest learning based segmentation techniques. Given the efficiency and robustness, our method has great potential in IVUS images processing and coronary artery segmentation and quantification.
中文翻译：
血管内超声图像中冠状动脉管腔分割的监督机器学习。
血管内超声 (IVUS) 已被广泛用于捕获冠状动脉内壁的横截面管腔框架。这种医学成像方式能够提供详细而有意义的管腔轮廓形状信息，这对于心血管疾病的临床诊断和分析非常重要。由于其令人印象深刻的结果，许多基于学习的技术最近变得非常流行用于冠状动脉分割。在这项工作中，设计了一种具有高精度和最小用户交互的冠状动脉管腔分割的监督机器学习方法。联合学习弱学习器所依赖的分类器并开发分类器的特征的完全判别流明分割方法。此外，介绍了这项工作中使用的梯度提升框架的理论支持及其二次近似。该算法在MICCAI 2011举办的IVUS挑战中流明边界检测的公共数据集上进行了测试，获得了更高的平均Jaccard相似度96.8%和更低的平均误差距离0.55（笛卡尔坐标系），相比之下显示出更高的精度到现有的基于学习的方法。此外，三个真实的患者 IVUS 数据集用于评估所提出的冠状动脉腔分割算法的性能，该算法显示可实现较低的腔面积百分比误差，分别为 1.861% ± 0.965%、1.968% ± 0.864% 和 1.671% ±与手动测量的流明面积（地面实况）相比，分别为 0.584%。发现所提出的流明分割方法优于最新的基于学习的分割技术。鉴于效率和稳健性，我们的方法在 IVUS 图像处理和冠状动脉分割和量化方面具有巨大潜力。
更新日期：2020-05-19
中文翻译：
血管内超声图像中冠状动脉管腔分割的监督机器学习。
血管内超声 (IVUS) 已被广泛用于捕获冠状动脉内壁的横截面管腔框架。这种医学成像方式能够提供详细而有意义的管腔轮廓形状信息，这对于心血管疾病的临床诊断和分析非常重要。由于其令人印象深刻的结果，许多基于学习的技术最近变得非常流行用于冠状动脉分割。在这项工作中，设计了一种具有高精度和最小用户交互的冠状动脉管腔分割的监督机器学习方法。联合学习弱学习器所依赖的分类器并开发分类器的特征的完全判别流明分割方法。此外，介绍了这项工作中使用的梯度提升框架的理论支持及其二次近似。该算法在MICCAI 2011举办的IVUS挑战中流明边界检测的公共数据集上进行了测试，获得了更高的平均Jaccard相似度96.8%和更低的平均误差距离0.55（笛卡尔坐标系），相比之下显示出更高的精度到现有的基于学习的方法。此外，三个真实的患者 IVUS 数据集用于评估所提出的冠状动脉腔分割算法的性能，该算法显示可实现较低的腔面积百分比误差，分别为 1.861% ± 0.965%、1.968% ± 0.864% 和 1.671% ±与手动测量的流明面积（地面实况）相比，分别为 0.584%。发现所提出的流明分割方法优于最新的基于学习的分割技术。鉴于效率和稳健性，我们的方法在 IVUS 图像处理和冠状动脉分割和量化方面具有巨大潜力。
京公网安备 11010802027423号