AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation

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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.
Funding Information
  • National Key R&D Program of China (2016YFC0104700)
  • National Natural Science Foundation of China (81571758)
  • Shenzhen Key Basic Research Project (JCYJ20180507184647636, JCYJ20170818142347251, JCYJ20170818094109846)
  • Royal Academy of Engineering (INSILEX CiET1819/19)
  • Chinese Academy of Sciences
  • European Commission (H2020-SC1-PM-16-2017-777119)

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