用深度学习自动估算超声多普勒角度，提升血流速度测量准确性。
Automated ultrasound doppler angle estimation using deep learning
- 基于2100张颈动脉超声图训练模型，融合预训练网络提取特征
- 最优模型误差低于临床可接受阈值，避免误判狭窄
- 适合希望提升超声诊断自动化水平的医疗科技开发者
角度估计是多普勒超声临床流程中测量血流速度的关键步骤。错误的角度估计被广泛认为是多普勒血流速度测量误差的主要原因。本文提出一种基于深度学习的自动多普勒角度估算方法。该方法使用2100张人类颈动脉超声图像（含数据增强）进行训练，采用五个预训练模型提取图像特征，并通过自定义浅层网络完成角度估计。独立由人工观察者进行角度测量作为对照。自动化与人工估计之间的平均绝对误差（MAE）在3.9°至9.4°之间。其中表现最佳模型的MAE低于临床可接受的多普勒角度误差阈值，从而避免将正常血流速度误判为狭窄。结果表明，基于深度学习的自动化角度估计具有应用潜力，未来可集成于商业超声设备的成像软件中。
原文摘要 · Abstract (English)
Angle estimation is an important step in the Doppler ultrasound clinical workflow to measure blood velocity. It is widely recognized that incorrect angle estimation is a leading cause of error in Doppler-based blood velocity measurements. In this paper, we propose a deep learning-based approach for automated Doppler angle estimation. The approach was developed using 2100 human carotid ultrasound images including image augmentation. Five pre-trained models were used to extract images features, and these features were passed to a custom shallow network for Doppler angle estimation. Independently, measurements were obtained by a human observer reviewing the images for comparison. The mean absolute error (MAE) between the automated and manual angle estimates ranged from 3.9° to 9.4° for the models evaluated. Furthermore, the MAE for the best performing model was less than the acceptable clinical Doppler angle error threshold thus avoiding misclassification of normal velocity values as a stenosis. The results demonstrate potential for applying a deep-learning based technique for automated ultrasound Doppler angle estimation. Such a technique could potentially be implemented within the imaging software on commercial ultrasound scanners.
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