论文标题
手术符号识别手术流程表图的图像分段
Hand-drawn Symbol Recognition of Surgical Flowsheet Graphs with Deep Image Segmentation
论文作者
论文摘要
围手术期数据对于研究不良手术结果的原因至关重要。在某些低至中收入的国家中，由于缺乏手术流程数字化，这些数据在计算上是无法访问的。在本文中，我们使用U-NET体系结构提出了一种深层的图像分割方法，该架构可以在Flow表格图上检测手绘符号。分割掩码输出后处理每个符号所特有的技术，以转换为数字值。 U-NET方法可以在适当的时间间隔内检测到心率和血压的符号，精度超过99％。与实际值相比，超过95％的预测在绝对误差范围内。深度学习模型的表现超过了匹配的模板，即使可用于训练集的带注释的图像少。
Perioperative data are essential to investigating the causes of adverse surgical outcomes. In some low to middle income countries, these data are computationally inaccessible due to a lack of digitization of surgical flowsheets. In this paper, we present a deep image segmentation approach using a U-Net architecture that can detect hand-drawn symbols on a flowsheet graph. The segmentation mask outputs are post-processed with techniques unique to each symbol to convert into numeric values. The U-Net method can detect, at the appropriate time intervals, the symbols for heart rate and blood pressure with over 99 percent accuracy. Over 95 percent of the predictions fall within an absolute error of five when compared to the actual value. The deep learning model outperformed template matching even with a small size of annotated images available for the training set.
