Research on automatic segmentation of the left ventricular echocardiogram and calculation of ejection fraction

In the diagnosis of clinical cardiovascular diseases, the calculation of left ventricular (LV) ejection fraction (EF) is a key step in the assessment of cardiac function. The accuracy of EF depends on the precise segmentation of the LV region. Traditional methods require clinicians to manually outline the LV boundaries in 2D ultrasound images, which is inefficient and subjective. To solve this problem, we propose a deep learning method for automatic segmentation of LV in echocardiography. The method fuses a time series feature enhancement module with a 3D convolutional structure, aiming to achieve a balance between the accuracy and computational efficiency. Further, based on the segmentation results, we also propose an algorithm to automatically calculate the left ventricular ejection fraction (LVEF). Experimental results on the EchoNet-Dynamic dataset show that our segmentation method works well on both 2D images and 3D videos. The average absolute error (MAE) of the computed EF is 0.08, which is lower than the range of normal physiological variability, confirming its potential value in clinical applications.