Guo, L, Lei, B, Chen, W et al. (7 more authors) (2021) Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography. Medical Image Analysis, 71. 102042. ISSN 1361-8415
Abstract
Paediatric echocardiography is a standard method for screening congenital heart disease (CHD). The segmentation of paediatric echocardiography is essential for subsequent extraction of clinical parameters and interventional planning. However, it remains a challenging task due to (1) the considerable variation of key anatomic structures, (2) the poor lateral resolution affecting accurate boundary definition, (3) the existence of speckle noise and artefacts in echocardiographic images. In this paper, we propose a novel deep network to address these challenges comprehensively. We first present a dual-path feature extraction module (DP-FEM) to extract rich features via a channel attention mechanism. A high- and low-level feature fusion module (HL-FFM) is devised based on spatial attention, which selectively fuses rich semantic information from high-level features with spatial cues from low-level features. In addition, a hybrid loss is designed to deal with pixel-level misalignment and boundary ambiguities. Based on the segmentation results, we derive key clinical parameters for diagnosis and treatment planning. We extensively evaluate the proposed method on 4,485 two-dimensional (2D) paediatric echocardiograms from 127 echocardiographic videos. The proposed method consistently achieves better segmentation performance than other state-of-the-art methods, whichdemonstratesfeasibility for automatic segmentation and quantitative analysis of paediatric echocardiography. Our code is publicly available at https://github.com/end-of-the-century/Cardiac.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier B.V. All rights reserved. This is an author produced version of an article published in Medical Image Analysis. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Paediatric echocardiography segmentation quantitative analysis; Attention mechanism; Feature fusion; Dual attention enhancement |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Sep 2021 10:46 |
Last Modified: | 20 Mar 2022 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.media.2021.102042 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178623 |