A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition
Abstract
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 × 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.
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Wang et al. and Yu et al. presented AI algorithms to automatically identify standard planes in 2D images.
“Wang et al. and Yu et al. presented AI algorithms to automatically identify standard planes in 2D images [97,98].”
A very deep CNN with a global average pooling strategy achieved significant performance improvement on limited training data in fetal standard plane recognition.
“In a specific fetal standard plane recognition task, a very deep CNN with a global average pooling (GAP) strategy achieved significant performance improvement on the limited training data [104,105].”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [105]
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- Authors
- Yu, Z., Tan, E.-L., Ni, D., Qin, J., Chen, S., Li, S., Lei, B., & Wang, T.
- Affiliations
- Shenzhen University (CN)
- Hong Kong Polytechnic University (HK)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2018-05
- Volume
- 22
- Issue
- 3
- Pages
- 874-885
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2614217642
- Cited by
- 109
- References
- 74
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 16 · 2022: 11 · 2021: 17 · 2020: 13 · 2019: 12 · 2018: 10 · 2017: 1
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Funders
- National Natural Science Foundation of China (81571758, 61571304, 61402296, 61427806)
- National Key Research and Develop Program (2016YFC0104703)
- Guangdong Medical (B2016094)
- Shenzhen Peacock Plan (KQTD2016053112051497)
- Shenzhen Key Basic Research (JCYJ20150525092940986, JCYJ20150525092940988)
- National Natural Science Foundation of Shenzhen University (827000197)
- 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
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- prenatal ultrasound, deep convolutional neural networks, transfer learning, data augmentation, prenatal diagnosis, intra-class variation, global average pooling
- MeSH terms
- Algorithms, Face, Female, Fetus, Humans, Image Interpretation, Computer-Assisted, Pregnancy, Ultrasonography, Prenatal, Neural Networks, Computer
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