Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography

Libao Guo, Baiying Lei, Weiling Chen, Jie Du, Alejandro F. Frangi, Jing Qin, Cheng Zhao, Pengpeng Shi, Bei Xia, Tianfu Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Article number102042
JournalMedical Image Analysis
Volume71
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Attention mechanism
  • Dual attention enhancement
  • Feature fusion
  • Paediatric echocardiography segmentation quantitative analysis

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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