Medical Ultrasound Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China.
Comput Math Methods Med. 2021 Aug 18;2021:3425893. doi: 10.1155/2021/3425893. eCollection 2021.
Carotid plaque echogenicity in ultrasound images has been found to be closely correlated with the risk of stroke in atherosclerotic patients. The automatic and accurate classification of carotid plaque echogenicity is of great significance for clinically estimating the stability of carotid plaques and predicting cardiovascular events. Existing convolutional neural networks (CNNs) can provide an automatic carotid plaque echogenicity classification; however, they require a fixed-size input image, while the carotid plaques are of varying sizes. Although cropping and scaling the input carotid plaque images is promising, it will cause content loss or distortion and hence reduce the classification accuracy. In this study, we redesign the spatial pyramid pooling (SPP) and propose multilevel strip pooling (MSP) for the automatic and accurate classification of carotid plaque echogenicity in the longitudinal section. The proposed MSP module can accept arbitrarily sized carotid plaques as input and capture a long-range informative context to improve the accuracy of classification. In our experiments, we implement an MSP-based CNN by using the visual geometry group (VGG) network as the backbone. A total of 1463 carotid plaques (335 echo-rich plaques, 405 intermediate plaques, and 723 echolucent plaques) were collected from Zhongnan Hospital of Wuhan University. The 5-fold cross-validation results show that the proposed MSP-based VGGNet achieves a sensitivity of 92.1%, specificity of 95.6%, accuracy of 92.1%, and F1-score of 92.1%. These results demonstrate that our approach provides a way to enhance the applicability of CNN by enabling the acceptance of arbitrary input sizes and improving the classification accuracy of carotid plaque echogenicity, which has a great potential for an efficient and objective risk assessment of carotid plaques in the clinic.
颈动脉斑块在超声图像中的回声特征与动脉粥样硬化患者发生中风的风险密切相关。自动且准确地对颈动脉斑块回声特征进行分类，对于临床上评估颈动脉斑块的稳定性和预测心血管事件具有重要意义。现有的卷积神经网络（CNN）可以提供自动的颈动脉斑块回声特征分类；然而，它们需要固定大小的输入图像，而颈动脉斑块的大小却各不相同。虽然裁剪和缩放输入的颈动脉斑块图像是有前景的，但这会导致内容丢失或失真，从而降低分类准确性。在本研究中，我们重新设计了空间金字塔池化（SPP），并提出了多层次条带池化（MSP），用于自动且准确地对颈动脉斑块的纵向切片进行回声特征分类。所提出的 MSP 模块可以接受任意大小的颈动脉斑块作为输入，并捕获长程信息上下文，以提高分类的准确性。在我们的实验中，我们使用视觉几何组（VGG）网络作为骨干，实现了一个基于 MSP 的 CNN。总共从武汉大学中南医院收集了 1463 个颈动脉斑块（335 个回声丰富斑块、405 个中间斑块和 723 个低回声斑块）。五折交叉验证结果表明，所提出的基于 MSP 的 VGGNet 实现了 92.1%的灵敏度、95.6%的特异性、92.1%的准确率和 92.1%的 F1 分数。这些结果表明，我们的方法通过允许接受任意输入大小并提高颈动脉斑块回声特征的分类准确性，为增强 CNN 的适用性提供了一种途径，这对于在临床上对颈动脉斑块进行高效和客观的风险评估具有很大的潜力。
