EchoGPK:基于先验知识引导的超声心动图轻量级图卷积分析方法OA北大核心CSTPCD

Accurate analysis of the left ventricular outline and ejection fraction through echocardiography holds sig-nificant diagnostic implications in cardiovascular diseases.However,current methodologies exhibit deficiencies such as a lack of correlation between left ventricular segmentation and ejection fraction prediction,susceptibility to outliers and abrupt variations in key points of left ventricular segmentation,substantial storage and computational overhead,and poor in-terpretability.In addressing these issues,this study proposes a lightweight graph convolutional method termed EchoGPK(Echo Guided by Priori Knowledge).Guided by prior knowledge encompassing cardiac structure,motion characteristics,and the similarity among adjacent myocardial regions,the approach incorporates a computationally efficient spiral aggrega-tion function and a deeply compressed multi-head eccentric aggregation decoder,achieving the lightweighting of the graph convolutional structure.Leveraging the common experiences of clinical practitioners,the method introduces a multi task ejection fraction prediction network that moderately utilizes left ventricular contours,establishing a correlation between left ventricular segmentation and ejection fraction prediction to enhance interpretability.By employing the graph convolutional neural network transmission characteristics to constrain the behavior of neighboring points,the generation of boundary outli-ers and abrupt variations is reduced.Experimental results on the large-scale public dataset EchoNet-Dynamic demonstrate that EchoGPK achieves a Dice score of 92.13％for left ventricular segmentation and a mean absolute error(MAE)of 3.92％for ejection fraction prediction.Furthermore,the method exhibits higher accuracy,superior parameter count and computa-tional efficiency compared to relevant approaches,affirming the effectiveness of prior knowledge in ultrasound medical im-age analysis.