The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes
Abstract
Quality control/assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP) is one of the most commonly used cardiac views, which was used in the diagnosis of heart anomalies in the early 1980s. In this paper, we propose a generic deep learning framework for automatic quality control of fetal US CFPs. The proposed framework consists of three networks: (1) a basic CNN (B-CNN), roughly classifying four-chamber views from the raw data; (2) a deeper CNN (D-CNN), determining the gain and zoom of the target images in a multi-task learning manner; and (3) the aggregated residual visual block net (ARVBNet), detecting the key anatomical structures on a plane. Based on the output of the three networks, overall quantitative score of each CFP is obtained, so as to achieve fully automatic quality control. Experiments on a fetal US dataset demonstrated our proposed method achieved a highest mean average precision (mAP) of 93.52% at a fast speed of 101 frames per second (FPS). In order to demonstrate the adaptability and generalization capacity, the proposed detection network (i.e., ARVBNet) has also been validated on the PASCAL VOC dataset, obtaining a highest mAP of 81.2% when input size is approximately 300 × 300.
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“First, most of the studies only used 4CV images as the input for their algorithms [55,57,61,62,63,67,70]”
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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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
(2016) · Cited by 31,590
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Research works printed but not linked: 2
- Branch, 2012, Guidelines for prenatal ultrasound examination, Chin. J. Med. Ultrasound (Electron. Ed.), 9, 574
- 10.5555/3298023.3298188 · DOI
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A review on deep-learning algorithms for fetal ultrasound-image analysis
Fiorentino, M. C., Villani, F. P., Cosmo, M. D., Frontoni, E., & Moccia, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102629 · Cited by 224
Applications of artificial intelligence in cardiovascular imaging
Maxime Sermesant, B., Delingette, H., Cochet, H., Jaı̈s, P., & Ayache, N. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00527-2 · Cited by 181
Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging
Komatsu, M., Sakai, A., Dozen, A., Shozu, K., Yasutomi, S., Machino, H., Asada, K., Kaneko, S., & Hamamoto, R. (2021) · Biomedicines · doi:10.3390/biomedicines9070720 · Cited by 122
The molecular mechanisms of cardiac development and related diseases
Li, Y., Du, J., Deng, S., Liu, B., Jing, X., Yan, Y., Liu, Y., Wang, J., Zhou, X., & She, Q. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02069-8 · Cited by 108
Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review
Akkus, Z., Aly, Y. H., Attia, I. Z., López-Jiménez, F. A., Arruda-Olson, A. M., Pellikka, P. A., Pislaru, S. V., Kane, G. C., Friedman, P. A., & Oh, J. K. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10071391 · Cited by 95
Metadata
- Authors
- Dong, J., Liu, S., Liao, Y., Wen, H., Lei, B., Li, S., & Wang, T.
- Affiliations
- Shenzhen University (CN)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2020-04
- Volume
- 24
- Issue
- 4
- Pages
- 931-942
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2980762210
- Cited by
- 104
- References
- 55
- Citations by year
- 2026: 17 · 2025: 18 · 2024: 19 · 2023: 19 · 2022: 14 · 2021: 15 · 2020: 2
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81571758, 81771922, 61871274, 61801305, 61501305)
- National Key Research and Development Program of China Stem Cell and Translational Research (2016YFC0104700)
- 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
- prenatal ultrasound, image quality control, automated quality assessment, deep learning, multi-task learning, convolutional neural networks, object detection
- MeSH terms
- Female, Fetal Heart, Humans, Image Processing, Computer-Assisted, Pregnancy, Quality Control, Ultrasonography, Prenatal, Neural Networks, Computer
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