Background and objective

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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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使用DenseNets进行语义分割，用于颈动脉超声斑块分割和CIMT估计。

背景和目标

超声图像中颈动脉内膜中层厚度（CIMT）的测量可用于检测动脉粥样硬化斑块的存在。通常，CIMT估计策略是半自动的，因为它需要：（1）手动检查超声图像以找到感兴趣区域（ROI）的位置，这是仅需要少量图像时的快速且有用的操作被测量；（2）在ROI中自动划定CIM区域。自动化过程的现有工作已复制了相同的两步结构，从而产生了两个连续的独立方法。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，该方法使我们可以对斑块进行分割，并以快速，有用的方式估计大型图像数据集的CIMT。

方法

我们的单步方法基于紧密连接的卷积神经网络（DenseNets），用于对整个图像进行语义分割。它具有两个显着的特征：（1）避免了ROI的定义；（2）由于在DenseNets中进行的特征图的级联，因此可以在完整的图像解释中捕获多尺度的上下文信息。一旦输入图像被分割，就可以使用CIMT估计和斑块检测的简单方法。

结果

所提出的方法已通过包含8000多个图像的大数据集（REGICOR）进行了验证，该数据集对应于颈动脉的两个区域：颈总动脉（CCA）和球茎。其中，已使用331张图像的子集来评估语义分割的性能（对于火车而言，≈90％，对于测试而言，约为10％）。实验结果表明，我们的方法优于文献中发现的其他深层模型和浅层方法。特别是，我们的CIMT估计在CCA和Bulb图像中的相关系数分别为0.81和CIMT平均误差分别为0.02和0.06 mm。此外，在CCA和Bulb中，噬菌斑检测的准确度分别为96.45％和78.09％。为了测试泛化能力，

结论

进行的验证表明，所提出的方法对于噬斑检测和CIMT测量均是准确而客观的。此外，该方法的鲁棒性和泛化能力已通过两个不同的数据集得到证明。