Abstract
Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance). To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN). AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features. The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context. Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map. We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively. AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis.
Highlights
Congenital heart disease (CHD) is a type of birth defect with abnormal heart and vessels structures, related to environmental and genetic factors of the fetus or the pregnant women[1]
As shown in Fig. 2, the proposed AIDAN consists of a spatial path, a context path, and an feature fusion module (FFM)
A convolutional block attention module (CBAM) is added to the spatial path and the context path for better feature extraction, denoted as SPA and CPA in Fig. 2, respectively
Summary
Congenital heart disease (CHD) is a type of birth defect with abnormal heart and vessels structures, related to environmental and genetic factors of the fetus or the pregnant women[1]. There are 1.5∼2 million children born with CHD according to the World Health Organization [2]. Krasuski [1] reported that the incidence of CHD in America is nearly 1%, and that in China is 1.42% [3]. Echocardiography is the primary examination method for CHD diagnosis. It is non-invasive, low-cost and suitable for real-time imaging. Accurate segmentation of cardiac anatomy in echocardiography images is
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