Myocardial Segmentation Algorithm of U-Net Network Based on Cardiac Ultrasound Images

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The myocardial state is always regarded as an important basis for identifying cardiac diseases. In order to assist physicians in diagnosis in an accurate manner, this paper proposes myocardial segmentation using a U-Net network based on cardiac ultrasound images. Firstly, we collected a large amount of clinical data and employed professional cardiac ultrasound imaging physicians to mark the myocardial regions as the gold standard. Then, we built an optimized U-Net network to establish the relationship between images and semantics to extract original image features. Finally, a newly fused loss function for training the network is created. According to the experiments, it shows that the accuracy, precision, and recall rate of U-Net indexes proposed in this paper reaches more than 96%, and MIOU more than 94%, which can effectively assist doctors in diagnosis in an accurate manner.