Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
Abstract
Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.
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A limitation of AI applications for fetal biometry is slow processing times.
“slow processing times [35,43]”
AI usage in standard plane acquisition has the benefit of feasibility of biometric assessment by non-experts.
“the feasibility of biometric assessment by non-experts [35,39,43]”
Both pre-trained feature extraction and fine-tuning strategies achieve excellent performance in several specific tasks.
“Both strategies achieve excellent performance in several specific tasks [77,78].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [78]
Baumgartner et al. and Chen et al. accomplished detection of 13 fetal standard views and the fetal abdominal, face, or four-chamber standard plane in 2D US images through transferred deep models.
“Baumgartner et al. [117,118] and Chen et al. [78,119] accomplished the detection of 13 fetal standard views (e.g., kidneys, brain, abdominal, spine, femur, and cardiac plane) and the fetal abdominal (or face and four-chamber view) standard plane in 2D US images through the transferred deep models, respectively.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [78]
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Self-citation
- Authors
- Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., & Heng, P. A.
- Affiliations
- Chinese University of Hong Kong (HK)
- Shenzhen University (CN)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2015-09
- Volume
- 19
- Issue
- 5
- Pages
- 1627-1636
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2061715187
- Cited by
- 379
- References
- 37
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 29 · 2023: 42 · 2022: 28 · 2021: 58 · 2020: 41 · 2019: 54 · 2018: 35 · 2017: 29 · 2016: 27 · 2015: 2
- FWCI
- 26.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Natural Science Foundation of China (81270707, 61233012)
- Shenzhen Key Basic Research Project (JCYJ20130329105033277, JCYJ20140509172609164)
- Shenzhen-Hong Kong Innovation Circle Funding Program (JSE201109150013A, SGLH20131010151755080)
- Hong Kong Innovation and Technology Fund (GHP/003/11SZ, GHP/002/13SZ)
- Research Grants Council of Hong Kong (CUHK412510)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- prenatal ultrasound, domain transfer, transfer learning, convolutional neural networks, limited training data, overfitting, medical image classification
- MeSH terms
- Abdomen, Female, Fetus, Humans, Image Processing, Computer-Assisted, Pregnancy, ROC Curve, Ultrasonography, Prenatal, Neural Networks, Computer
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