Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism
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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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”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [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
- Publisher
- SAGE Publications
- 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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