The DOI matches its Crossref record; 69 of the 74 references it cites were found in a registry or an index.
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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In the citing paper
“Wang et al. and Yu et al. presented AI algorithms to automatically identify standard planes in 2D images [97,98].”
In the citing paper
“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]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Research works printed but not linked: 2
- lin, 2013, Network in network, arXiv 1312 4400
- chen, 0, Automatic fetal ultrasound standard pane detection using knowledge transferred recurrent neural networks, Med Imag Comput Comput Assist Interv, 507
Cited by
Sorted by citations
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
Self-citationAkkus, Z., Cai, J. C., Boonrod, A., Zeinoddini, A., Weston, A. D., Philbrick, K. A., & Erickson, B. J. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.004 · Cited by 342
Global guidance network for breast lesion segmentation in ultrasound images
Xue, C., Zhu, L., Fu, H., Hu, X., Li, X., Zhang, H., & Heng, P. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101989 · Cited by 241
Automatic Fetal Ultrasound Standard Plane Recognition Based on Deep Learning and IIoT
Pu, B., Li, K., Li, S., & Zhu, N. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3069470 · Cited by 221
Self-citationArtificial intelligence in ultrasound
Shen, Y., Chen, L., Yue, W., & Xu, H. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109717 · Cited by 226
Metadata
- 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
- 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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