Fetal facial standard plane recognition via very deep convolutional networks
Abstract
The accurate recognition of fetal facial standard plane (FFSP) (i.e., axial, coronal and sagittal plane) from ultrasound (US) images is quite essential for routine US examination. Since the labor-intensive and subjective measurement is too time-consuming and unreliable, the development of the automatic FFSP recognition method is highly desirable. Different from the previous methods, we leverage a general framework to recognize the FFSP from US images automatically. Specifically, instead of using the previous hand-crafted visual features, we utilize the recent developed deep learning approach via very deep convolutional networks (DCNN) architecture to represent fine-grained details of US image. Also, very small (3×3) convolution filters are adopted to improve the performance. The evaluation of our FFSP dataset shows the superiority of our method over the previous studies and achieves the state-of-the-art FFSP recognition results.
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Four studies evaluated AI assessment of fetal lung ultrasound for assessment of fetal lung maturity, prediction of neonatal respiratory morbidity and assessment of correlation of fetal lung texture with gestational age.
“Four studies evaluated AI assessment of fetal lung ultrasound for assessment of fetal lung maturity, prediction of neonatal respiratory morbidity and assessment of correlation of fetal lung texture with gestational age.[111][112][113][114]”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [113]
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- Authors
- Yu, Z., Ni, D., Chen, S., Li, S., Wang, T., & Lei, B.
- Venue
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2016-08
- Volume
- 2016
- Pages
- 627-630
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2533041079
- Semantic Scholar
- 2a19608262e17229d68383922e4fc58a8ad7b902
- Cited by
- 22
- References
- 10
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 1
- FWCI
- 0.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- prenatal ultrasound
- MeSH terms
- Face, Fetus, Humans, Learning, Pattern Recognition, Automated, Ultrasonography, Prenatal, Neural Networks, Computer
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