DLFFNet: A new dynamical local feature fusion network for automatic aortic valve calcification recognition using echocardiography

Abstract

Background and ObjectiveAortic valve calcification (AVC) is a strong predictor of adverse cardiovascular events and is correlated with the degree of coronary artery stenosis. Generally, AVC is identified by echocardiography using visual “eyeballing”, which results in huge differences between observers and requires a long time to learn. Therefore, accurately identifying AVC from echocardiographic images is a challenging task due to the interference of various factors. MethodIn this paper, we built a dynamical local feature fusion net capable of processing echocardiography to recognize AVC automatically. We proposed high-echo area which were segmented by a U-Net. Meanwhile, we fine-tuned the segmentation results by adding brightness in the mask tuning module in order to dynamically adjust the selection of local features. To better fuse multi-level and multi-scale information, we designed a pyramid-based two-branch feature fusion module in classification, which enables the network to better integrate global and local semantic representations. In addition, for the echocardiographic data collected by different devices and doctors, inconsistent aortic valve position with a small occupied area, a unified preprocessing algorithm was designed. ResultsTo highlight the effectiveness of the proposed approach, we compared several state-of-the-art methods on the same ultrasound dataset. The 231 patients with short-axis views of the aortic valve images were collected and labeled (masked) by experienced ultrasound doctors from The First Hospital of China Medical University. The accuracy, precision, sensitivity, specificity, and F1 score, micro-AUC, and macro-AUC of the model for the test dataset were, 82.40%, 82.50%, 82.50%, 91.23%, 82.47%, 92.39%, and 92.25%, respectively. ConclusionsThe results showed the possibility of using echocardiography to examine AVC automatically and verified by visualization methods that the Region of Interest of the model is consistent with the observed region of the experts.