Cited by 21Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and EducationAdvanced Neural Network Applications

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

Xi, Jianing, Chen, Jiangang, Wang, Zhao, Ta, Dean, Lu, Bing, Deng, Xuedong, Li, Xuelong, Huang, Qinghua·2021·Ultrasonic Imaging

Abstract

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

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Crossref · 10.1177/01617346211042526

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29 of 29 references verified0 citation errors · 0 not checkable

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  • Xi et al. proposed a system for automatic segmentation of the fetal heart and lungs from ultrasound images.

    “A study by Xi et al. proposed a system for automatic segmentation of the fetal heart and lungs from ultrasound images.84”

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Authors
Xi, J., Chen, J., Wang, Z., Ta, D., Lu, B., Deng, X., Li, X., & Huang, Q.
Affiliations
  • Northwestern Polytechnical University (CN)
  • East China Normal University (CN)
  • Fudan University (CN)
  • Nanjing Medical University (CN)
Corresponding author
Huang, Qinghua
Venue
Ultrasonic Imaging
Published
2021-09-01
Volume
43
Issue
6
Pages
308-319
ISSN
  • 0161-7346 (ISSN-L)
  • 1096-0910
OpenAlex
W3196582844
Cited by
21
References
30
Citations by year
2026: 3 · 2025: 2 · 2024: 2 · 2023: 11 · 2022: 3
FWCI
1.44
Type
journal article
Language
en
Open access
closed
Locations
  • Ultrasonic Imaging (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
ultrasound images, multi-scale feature extraction, attention mechanism, skip connections, U-Net, semantic segmentation, congenital malformations
MeSH terms
Fetal Heart, Image Processing, Computer-Assisted, Lung, Ultrasonography, Neural Networks, Computer

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