Xiao H, Fang W, Lin M, Zhou Z, Fei H, Chen C
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Guangzhou Shangyi Network Information Technology Co., Ltd., Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 Feb 20;44(2):387-396. doi: 10.12122/j.issn.1673-4254.2024.02.22.
To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.
We proposed a two-stage carotid plaque detection method based on deep convolutional neural network (SM-YOLO).A series of algorithms such as median filtering, histogram equalization, and Gamma transformation were used to preprocess the dataset to improve image quality. In the first stage of the model construction, a candidate plaque set was built based on the YOLOX_l target detection network, using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes. In the second stage, the Histogram of Oriented Gradient (HOG) features and Local Binary Pattern (LBP) features were extracted and fused, and a Support Vector Machine (SVM) classifier was used to screen the candidate plaque set to obtain the final detection results. This model was compared quantitatively and visually with several target detection models (YOLOX_l, SSD, EfficientDet, YOLOV5_l, Faster R-CNN).
SM-YOLO achieved a recall of 89.44%, an accuracy of 90.96%, a F1-Score of 90.19%, and an AP of 92.70% on the test set, outperforming other models in all performance indicators and visual effects. The constructed model had a much shorter detection time than the Faster R-CNN model (only one third of that of the latter), thus meeting the requirements of real-time detection.
The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.
开发一种在超声图像中准确识别多尺度颈动脉斑块的方法。
我们提出了一种基于深度卷积神经网络的两阶段颈动脉斑块检测方法（SM-YOLO）。使用中值滤波、直方图均衡化和伽马变换等一系列算法对数据集进行预处理，以提高图像质量。在模型构建的第一阶段，基于YOLOX_l目标检测网络构建候选斑块集，采用多尺度图像训练和多尺度图像预测策略，以适应不同形状和大小的颈动脉斑块。在第二阶段，提取并融合定向梯度直方图（HOG）特征和局部二值模式（LBP）特征，并使用支持向量机（SVM）分类器对候选斑块集进行筛选，以获得最终检测结果。将该模型与几种目标检测模型（YOLOX_l、SSD、EfficientDet、YOLOV5_l、Faster R-CNN）进行了定量和可视化比较。
SM-YOLO在测试集上的召回率为89.44%，准确率为90.96%，F1分数为90.19%，平均精度为92.70%，在所有性能指标和视觉效果上均优于其他模型。所构建的模型检测时间比Faster R-CNN模型短得多（仅为后者的三分之一），从而满足实时检测的要求。
所提出的颈动脉斑块检测方法在超声图像中准确识别颈动脉斑块方面具有良好性能。
