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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“First, most of the studies only used 4CV images as the input for their algorithms [55,57,61,62,63,67,70]”
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
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
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556
Rethinking Atrous Convolution for Semantic Image Segmentation
Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1706.05587 · Cited by 7,517
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Receptive Field Block Net for Accurate and Fast Object Detection
Liu, S., Huang, D., & Wang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_24 · Cited by 1,885
Self-citationDeep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,789
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,725
DSSD : Deconvolutional Single Shot Detector
Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017) · arXiv · doi:10.48550/arxiv.1701.06659 · Cited by 1,624
Overview of deep learning in medical imaging
Suzuki, K. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0406-5 · Cited by 1,131
Recent advances in deep learning for speech research at Microsoft
Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639345 · Cited by 801
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-citationHu, P., & Ramanan, D. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.166 · Cited by 719
DSOD: Learning Deeply Supervised Object Detectors from Scratch
Shen, Z., Liu, Z., Li, J., Jiang, Y., Chen, Y., & Xue, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.212 · Cited by 678
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
Wang, X., Shrivastava, A., & Gupta, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.324 · Cited by 655
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2712367 · Cited by 416
FSSD: Feature Fusion Single Shot Multibox DetectorPreprint
Li, Z., Yang, L., & Zhou, F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00960 · Cited by 405
Enhancement of SSD by concatenating feature maps for object detection
Jeong, J., Park, H., & Kwak, N. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.76 · Cited by 394
Rigid-Motion Scattering for Texture ClassificationPreprint
Sifre, L., & Mallat, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.1687 · Cited by 371
Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities
Moeskops, P., Wolterink, J. M., van der Velden, B. H. M., Gilhuijs, K. G. A., Leiner, T., Viergever, M. A., & Išgum, I. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_55 · Cited by 354
Yagel, S., Cohen, S. M., & Achiron, R. (2001) · Ultrasound in Obstetrics and Gynecology · doi:10.1046/j.1469-0705.2001.00414.x · Cited by 299
Computational neuroimaging and population receptive fields
Wandell, B. A., & Winawer, J. A. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.03.009 · Cited by 255
FUIQA: Fetal Ultrasound Image Quality Assessment with Deep Convolutional Networks
Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., & Ni, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2671898 · Cited by 242
Samala, R. K., Chan, H., Hadjiiski, L. M., Helvie, M. A., Kenny, H., & Richter, C. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa93d4 · Cited by 231
Chen, H., Dou, Q., Ni, D., Cheng, J.-Z., Qin, J., Li, S., & Heng, P.-A. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24553-9_62 · Cited by 180
Self-citationUltrasound Standard Plane Detection Using a Composite Neural Network Framework
Chen, H., Wu, L., Dou, Q., Qin, J., Li, S., Cheng, J.-Z., Ni, D., & Heng, P.-A. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2685080 · Cited by 164
Self-citationSalomon, L. J., Bernard, J. P., Duyme, M., Doris, B., Mas, N., & Ville, Y. G. (2005) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.2665 · Cited by 146
Abdi, A. H., Luong, C. L., Tsang, T. S. M., Allan, G., Nouranian, S., Jue, J., Hawley, D., Fleming, S., Gin, K., Swift, J. L., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2690836 · Cited by 141
Lange, L., Sahn, D. J., Allen, H. D., Goldberg, S. J., Anderson, C., & Giles, H. (1980) · Circulation · doi:10.1161/01.cir.62.4.799 · Cited by 105
The importance of quality management in fetal measurement
Dudley, N. J., & Chapman, E. (2002) · Ultrasound in Obstetrics & Gynecology · doi:10.1046/j.0960-7692.2001.00549.x · Cited by 94
Salomon, L. J., Winer, N., Bernard, J. P., & Ville, Y. (2008) · Prenatal Diagnosis · doi:10.1002/pd.2016 · Cited by 85
Del Bianco, A., Russo, S., Lacerenza, N., Rinaldi, M., Rinaldi, G., Nappi, L., & Greco, P. (2006) · Journal of Perinatal Medicine · doi:10.1515/jpm.2006.059 · Cited by 67
A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat
Maraci, M. A., Bridge, C. P., Napolitano, R., Papageorghiou, A. T., & Noble, J. A. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.003 · Cited by 65
Abdi, A. H., Luong, C. L., Tsang, T. S. M., Jue, J., Gin, K., Yeung, D. F., Hawley, D., Rohling, R. N., & Abolmaesumi, P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_35 · Cited by 44
Quality control of fetal ultrasound images: Detection of abdomen anatomical landmarks using AdaBoost
Rahmatullah, B., Sarris, I., Papageorghiou, A. T., & Noble, J. A. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872342 · Cited by 37
A Review of Findings in Fetal Cardiac Section Drawings
Jeanty, P., Chaoui, R., Tihonenko, I., & Grochal, F. (2007) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2007.26.11.1601 · Cited by 14
Quality control of prenatal ultrasound
Salomon, L. J., & Ville, Y. G. (2005) · The ultrasound review of obstetrics & gynecology/Ultrasound review of obstetrics and gynecology · doi:10.3109/14722240500415419 · Cited by 11
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
Cited by
Sorted by citations
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
