A Semi-supervised Four-Chamber Echocardiographic Video Segmentation Algorithm Based on Multilevel Edge Perception and Calibration Fusion

Objective Echocardiographic videos are commonly used for automatic semantic segmentation of endocardium, which is crucial in evaluating cardiac function and assisting doctors to make accurate diagnoses of heart disease. However, this task faces two distinct challenges: one is the edge blurring, which is caused by the presence of speckle noise or excessive de-noising operation, and the other is the lack of an effective feature fusion approach for multilevel features for obtaining accurate endocardium. Methods In this study, a deep learning model, based on multilevel edge perception and calibration fusion is proposed to improve the segmentation performance. First, a multilevel edge perception module is proposed to comprehensively extract edge features through both a detail branch and a semantic branch to alleviate the adverse impact of noise. Second, a calibration fusion module is proposed that calibrates and integrates various features, including semantic and detailed information, to maximize segmentation performance. Furthermore, the features obtained from the calibration fusion module are stored by using a memory architecture to achieve semi-supervised segmentation through both labeled and unlabeled data. Results Our method is evaluated on two public echocardiography video data sets, achieving average Dice coefficients of 93.05% and 93.93%, respectively. Additionally, we validated our method on a local hospital clinical data set, achieving a Pearson correlation of 0.765 for predicting left ventricular ejection fraction. Conclusion The proposed model effectively solves the challenges encountered in echocardiography by using semi-supervised networks, thereby improving the segmentation accuracy of the ventricles. This indicates that the proposed model can assist cardiologists in obtaining accurate and effective research and diagnostic results.