Dept. de Matemátiques i Informática, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Spain.
Dept. de Matemátiques i Informática, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, Barcelona, 08007, Spain; Dept. Epidemiologia i Salut Pública, IMIM, Institut Hospital del Mar d'Investigacions Médiques, Dr. Aiguader 88, Barcelona, 08003, Spain; CIBER Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Monforte de Lemos 3-5, Pabellón 11, Madrid, 28029, Spain.
Comput Methods Programs Biomed. 2022 Aug;223:106954. doi: 10.1016/j.cmpb.2022.106954. Epub 2022 Jun 15.
The detection and delineation of atherosclerotic plaque are usually manually performed by medical experts on the carotid artery. Evidence suggests that this manual process is subject to errors and has a large variability between experts, equipment, and datasets. This paper proposes a robust end-to-end framework for automatic atherosclerotic plaque detection.
The proposed framework is composed of: (1) a semantic segmentation model based on U-Net, with EfficientNet as the backbone, that obtains a segmentation mask with the carotid intima-media region; and (2) a convolutional neural network designed using Bayesian optimization that simultaneously performs a regression to get the average and maximum carotid intima media thickness, and a classification to determine the presence of plaque.
Our approach improves the state-of-the-art in both co and bulb territories in the REGICOR database, with more than 8000 images, while providing predictions in real-time. The correlation coefficient was 0.89 in the common carotid artery and 0.74 for bulb region, and the F1 score for atherosclerotic plaque detecting was 0.60 and 0.59, respectively. The experimentation carried out includes a comparison with other fully automatic methods for carotid intima media thickness estimation found in the literature. Additionally, we present an extensive experimental study to evaluate the robustness of our proposal, as well as its suitability and efficiency compared to different versions of the framework.
The proposed end-to-end framework significantly improves the automatic characterization of atherosclerotic plaque. The generation of the segmented mask can be helpful for practitioners since it allows them to evaluate and interpret the model's results by visual inspection. Furthermore, the proposed framework overcomes the limitations of previous research based on ad-hoc post-processing, which could lead to overestimations in the case of oblique forms of the carotid artery.
通常由医学专家对颈动脉进行人工检测和描绘动脉粥样硬化斑块。有证据表明，这种手动过程容易出错，并且专家、设备和数据集之间存在很大的可变性。本文提出了一种用于自动动脉粥样硬化斑块检测的端到端稳健框架。
所提出的框架由以下两部分组成：(1)基于 U-Net 的语义分割模型，以 EfficientNet 为骨干，获得颈动脉内-中膜区域的分割掩模；(2)使用贝叶斯优化设计的卷积神经网络，同时进行回归以获得平均和最大颈动脉内-中膜厚度，并进行分类以确定斑块的存在。
我们的方法在 REGICOR 数据库中超过 8000 张图像的 co 和 bulb 区域均提高了现有技术水平，同时实现了实时预测。在颈总动脉和球部区域的相关系数分别为 0.89 和 0.74，动脉粥样硬化斑块检测的 F1 分数分别为 0.60 和 0.59。进行的实验包括与文献中其他用于颈动脉内-中膜厚度估计的全自动方法进行比较。此外，我们还进行了广泛的实验研究，以评估我们的方法的稳健性，以及与不同版本的框架相比的适用性和效率。
所提出的端到端框架可显著提高动脉粥样硬化斑块的自动特征描述。分割掩模的生成有助于从业人员通过目视检查评估和解释模型的结果。此外，所提出的框架克服了以前基于特定后处理的研究的局限性，这可能导致在颈动脉的斜形形式的情况下出现高估。
