CIBER Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Monforte de Lemos 3-5, Pabellón 11, 28029 Madrid, Spain; IMIM, Institut Hospital del Mar d'Investigacions Mèdiques, Dr. Aiguader 88, 08003 Barcelona, Spain; Departament de Matemà tiques i Informà tica, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain.
Department of Computer Science, Universidad de Oviedo, Campus de Gijón s/n, 33203 Gijón, Spain.
Artif Intell Med. 2020 Mar;103:101784. doi: 10.1016/j.artmed.2019.101784. Epub 2019 Dec 31.
The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: (1) a manual examination of the ultrasound image for the localization of a region of interest (ROI), a fast and useful operation when only a small number of images need to be measured; and (2) an automatic delineation of the CIM region within the ROI. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. In this work, we propose a fully automatic single-step approach based on semantic segmentation that allows us to segment the plaque and to estimate the CIMT in a fast and useful manner for large data sets of images.
Our single-step approach is based on densely connected convolutional neural networks (DenseNets) for semantic segmentation of the whole image. It has two remarkable characteristics: (1) it avoids ROI definition, and (2) it captures multi-scale contextual information in the complete image interpretation, due to the concatenation of feature maps carried out in DenseNets. Once the input image is segmented, a straightforward method for CIMT estimation and plaque detection is applied.
The proposed method has been validated with a large data set (REGICOR) of more than 8000 images, corresponding to two territories of the carotid artery: common carotid artery (CCA) and bulb. Among them, a subset of 331 images has been used to evaluate the performance of semantic segmentation (≈90% for train, ≈10% for test). The experimental results demonstrated that our method outperforms other deep models and shallow approaches found in the literature. In particular, our CIMT estimation reaches a correlation coefficient of 0.81, and a CIMT mean error of 0.02 and 0.06 mm in CCA and Bulb images, respectively. Furthermore, the accuracy for plaque detection is 96.45% and 78.09% in CCA and Bulb, respectively. To test the generalization power, the method has also been tested with another data set (NEFRONA) that includes images acquired with different equipment.
The validation carried out demonstrates that the proposed method is accurate and objective for both plaque detection and CIMT measurement. Moreover, the robustness and generalization capacity of the method have been proven with two different data sets.
通过超声图像测量颈动脉内膜中层厚度（CIMT）可用于检测动脉粥样硬化斑块的存在。通常，CIMT 的估计策略是半自动的，因为它需要：（1）手动检查超声图像以定位感兴趣区域（ROI），当只需要测量少量图像时，这是一项快速有用的操作；（2）在 ROI 内自动划定 CIM 区域。现有的自动化方法复制了相同的两步结构，导致了两种连续的独立方法。在这项工作中，我们提出了一种基于语义分割的全自动单步方法，该方法允许我们快速有效地分割斑块并估计 CIMT，适用于大型图像数据集。
我们的单步方法基于密集连接卷积神经网络（DenseNets）进行全图像语义分割。它有两个显著的特点：（1）它避免了 ROI 的定义；（2）由于 DenseNets 中进行的特征图拼接，它可以捕获完整图像解释中的多尺度上下文信息。一旦输入图像被分割，就可以应用一种简单的 CIMT 估计和斑块检测方法。
该方法已经在一个包含 8000 多张图像的大型数据集（REGICOR）上进行了验证，这些图像对应于颈动脉的两个区域：颈总动脉（CCA）和球部。其中，使用了 331 张图像的子集来评估语义分割的性能（≈90%用于训练，≈10%用于测试）。实验结果表明，我们的方法优于文献中发现的其他深度模型和浅层方法。特别是，我们的 CIMT 估计达到了 0.81 的相关系数，在 CCA 和球部图像中，CIMT 的平均误差分别为 0.02 和 0.06mm。此外，CCA 和球部的斑块检测准确率分别为 96.45%和 78.09%。为了测试泛化能力，该方法还在另一个包含使用不同设备获取的图像的数据集（NEFRONA）上进行了测试。
所进行的验证表明，该方法在斑块检测和 CIMT 测量方面既准确又客观。此外，该方法具有稳健性和泛化能力，已经在两个不同的数据集上得到了证明。
