Ultrasound Standard Plane Detection Using a Composite Neural Network Framework
Abstract
Ultrasound (US) imaging is a widely used screening tool for obstetric examination and diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is very crucial for substantial biometric measurement and diagnosis. However, the standard plane acquisition is a labor-intensive task and requires operator equipped with a thorough knowledge of fetal anatomy. Therefore, automatic approaches are highly demanded in clinical practice to alleviate the workload and boost the examination efficiency. The automatic detection of standard planes from US videos remains a challenging problem due to the high intraclass and low interclass variations of standard planes, and the relatively low image quality. Unlike previous studies which were specifically designed for individual anatomical standard planes, respectively, we present a general framework for the automatic identification of different standard planes from US videos. Distinct from conventional way that devises hand-crafted visual features for detection, our framework explores in- and between-plane feature learning with a novel composite framework of the convolutional and recurrent neural networks. To further address the issue of limited training data, a multitask learning framework is implemented to exploit common knowledge across detection tasks of distinctive standard planes for the augmentation of feature learning. Extensive experiments have been conducted on hundreds of US fetus videos to corroborate the better efficacy of the proposed framework on the difficult standard plane detection problem.
Integrity signals
Registry
Cites
Statistics
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?
A limitation of AI applications for fetal biometry is lack of training for algorithms with pathological cases.
“lack of training for algorithms with pathological cases [28,35]”
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 · see citing works
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 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
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 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
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 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
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 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv · doi:10.48550/arxiv.1502.02791 · Cited by 4,505 · see citing works
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451 · see citing works
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120 · see citing works
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894 · see citing works
A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation
Goutte, C., & Gaussier, E. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31865-1_25 · Cited by 2,285 · see citing works
A Novel Connectionist System for Unconstrained Handwriting Recognition
Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Horst Bunke, A., & Schmidhuber, J. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.137 · Cited by 1,984 · see citing works
Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies
(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544037.ch14 · Cited by 1,312 · see citing works
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528129 · Cited by 679 · see citing works
Self-citationGrossberg, S. (2013) · Scholarpedia · doi:10.4249/scholarpedia.1888 · Cited by 602 · see citing works
DCAN: Deep contour-aware networks for object instance segmentation from histology images
Chen, H., Qi, X., Yu, L., Dou, Q., Qin, J., & Heng, P. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.11.004 · Cited by 502 · see citing works
Self-citationGradient-based learning algorithms for recurrent networks and their computational complexity
Williams, R. J., & Zipser, D. (1995) · Cited by 413 · see citing works
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., & Heng, P. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2425041 · Cited by 379 · see citing works
Self-citation3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J., & Heng, P.-A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_18 · Cited by 344 · see citing works
Self-citationAIUM Practice Guideline for the Performance of Obstetric Ultrasound Examinations
(2013) · Journal of Ultrasound in Medicine · doi:10.7863/ultra.32.6.1083 · Cited by 338 · see citing works
Learning to ExecutePreprint
Zaremba, W., & Sutskever, I. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.4615 · Cited by 314 · see citing works
Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks
Chen, H., Dou, Q., Wang, X., Qin, J., & Heng, P. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10140 · Cited by 264 · see citing works
Self-citationDropout Training as Adaptive RegularizationPreprint
Wager, S., Wang, S., & Liang, P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.1493 · Cited by 260 · see citing works
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 · see citing works
Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.928917 · Cited by 205 · see citing works
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 · see citing works
Self-citationNatural Image Bases to Represent Neuroimaging Data
Gupta, A., Ayhan, M. S., & Maida, A. S. (2013) · Cited by 170 · see citing works
Shi, J., Zhou, S., Liu, X., Zhang, Q., Lu, M., & Wang, T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.074 · Cited by 166 · see citing works
Salomon, 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 · see citing works
Chen, S., Qin, J., Ji, X., Lei, B., Wang, T., Ni, D., & Cheng, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2629462 · Cited by 142 · see citing works
Self-citationChen, H., Zheng, Y., Park, J., Heng, P., & Zhou, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_56 · Cited by 114 · see citing works
Self-citation3D Deeply Supervised Network for Automatic Liver Segmentation from CT VolumesPreprint
Dou, Q., Chen, H., Jin, Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.00582 · Cited by 106 · see citing works
Self-citationThe 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 · see citing works
Utility of Portable Ultrasound in a Community in Ghana
Spencer, J. K., & Adler, R. S. (2008) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2008.27.12.1735 · Cited by 88 · see citing works
Shi, J., Wu, J., Li, Y., Zhang, Q., & Ying, S. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2602823 · Cited by 80 · see citing works
Abuhamad, A., Falkensammer, P., Reichartseder, F., & Zhao, Y. (2007) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.5228 · Cited by 69 · see citing works
Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search
Ni, D., Yang, X., Chen, X., Chin, C.-T., Chen, S., Heng, P. A., Li, S., Qin, J., & Wang, T. (2014) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2014.06.006 · Cited by 65 · see citing works
3D Fully Convolutional Networks for Intervertebral Disc Localization and Segmentation
Chen, H., Dou, Q., Wang, X., Qin, J., Cheng, J. C. Y., & Heng, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-43775-0_34 · Cited by 57 · see citing works
Self-citationIntegration of Local and Global Features for Anatomical Object Detection in Ultrasound
Rahmatullah, B., Papageorghiou, A. T., & Noble, J. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_50 · Cited by 55 · see citing works
Zhang, L., Chen, S., Chin, C. T., Wang, T., & Li, S. (2012) · Medical Physics · doi:10.1118/1.4736415 · Cited by 53 · see citing works
Self-citationSofka, M., Zhang, J., Good, S., Zhou, S. K., & Comaniciu, D. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2301936 · Cited by 40 · see citing works
Localizing target structures in ultrasound video – A phantom study
Kwitt, R., Vasconcelos, N. M., Razzaque, S., & Aylward, S. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.05.003 · Cited by 39 · see citing works
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 · see citing works
Qiao, H., Li, Y., Li, F., Xi, X., & Wu, W. L. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2476706 · Cited by 37 · see citing works
Calin, O. L. (2020) · Springer series in the data sciences · doi:10.1007/978-3-030-36721-3_17 · Cited by 36 · see citing works
Chen, S., Ni, D., Qin, J., Lei, B., Wang, T., & Cheng, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_7 · Cited by 35 · see citing works
Self-citationSelective Search and Sequential Detection for Standard Plane Localization in Ultrasound
Ni, D., Li, T., Yang, X., Qin, J., Li, S., Chin, C.-T., Ouyang, S., Wang, T., & Chen, S. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-41083-3_23 · Cited by 33 · see citing works
Self-citationSearching for Structures of Interest in an Ultrasound Video Sequence
Maraci, M. A., Napolitano, R., Papageorghiou, A. T., & Noble, J. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10581-9_17 · Cited by 33 · see citing works
Three‐dimensional Fetal Sonography
Benacerraf, B. R. (2002) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2002.21.10.1063 · Cited by 33 · see citing works
Zhang, Y., Lv, J. C., & Zhang, L. (2006) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2005.854500 · Cited by 22 · see citing works
Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer
Chen, H., Ni, D., Yang, X., Li, S., & Heng, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10581-9_16 · Cited by 17 · see citing works
Self-citationThe Practice of Ultrasound: A Step-by-Step Guide to Abdominal Scanning
Gangarosa, L. M. (2005) · Gastroenterology · doi:10.1053/j.gastro.2005.08.038 · Cited by 17 · see citing works
Pavlin, C. J. (2013) · Elsevier eBooks · doi:10.1016/b978-1-4557-1069-0.00038-4 · Cited by 6 · see citing works
Design and application of recurrent neural networks
Yi, D. (2009) · Computer Engineering and Applications Journal · Cited by 3 · see citing works
Flagged first, then by citations
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 · see citing works
Self-citationSV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network
Jin, Y., Dou, Q., Chen, H., Yu, L., Qin, J., Fu, C.-W., & Heng, P.-A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2787657 · Cited by 316 · see citing works
Self-citationIntroduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology
Drukker, L., Noble, J. A., & Papageorghiou, A. T. (2020) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.22122 · Cited by 240 · see citing works
Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal
Xie, H. N., Wang, N., He, M., Zhang, L. H., Cai, H. M., Xian, J. B., Lin, M. F., Zheng, J., & Yang, Y. Z. (2020) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.21967 · Cited by 176 · see citing works
Multi-task learning for quality assessment of fetal head ultrasound images
Lin, Z., Li, S., Ni, D., Liao, Y., Wen, H., Du, J., Chen, S., Wang, T., & Lei, B. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101548 · Cited by 129 · see citing works
Self-citationYu, Z., Tan, E.-L., Ni, D., Qin, J., Chen, S., Li, S., Lei, B., & Wang, T. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2705031 · Cited by 109 · see citing works
Self-citationA Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes
Dong, J., Liu, S., Liao, Y., Wen, H., Lei, B., Li, S., & Wang, T. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2948316 · Cited by 104 · see citing works
Self-citationUse of artificial intelligence and deep learning in fetal ultrasound imaging
Ramirez Zegarra, R., & Ghi, T. (2023) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.26130 · Cited by 102 · see citing works
van den Heuvel, T. L. A., Petros, H., Santini, S., de Korte, C. L., & van Ginneken, B. (2019) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2018.09.015 · Cited by 91 · see citing works
Artificial intelligence in obstetric ultrasound: A scoping review.
Horgan, R., Nehme, L., & Abuhamad, A. (2023) · Prenatal Diagnosis · doi:10.1002/pd.6411 · Cited by 72 · see citing works
“anatomical planes including the abdomen, axial plane of the face and four chamber cardiac view.(110) Twenty‐five studies were classified as other uses of AI in ob-”
MethodSridar, P., Kumar, A., Quinton, A., Nanan, R., Kim, J., & Krishnakumar, R. (2019) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2018.11.016 · Cited by 70 · see citing works
Lin, M., He, X., Guo, H., He, M., Zhang, L., Xian, J., Lei, T., Xu, Q., Zheng, J., Feng, J., et al. (2022) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.24843 · Cited by 65 · see citing works
Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks
Xie, B., Lei, T., Wang, N., Cai, H., Xian, J., He, M., Zhang, L., & Xie, H. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02182-3 · Cited by 65 · see citing works
Artificial Intelligence in Prenatal Ultrasound Diagnosis
He, F., Wang, Y., Xiu, Y., Zhang, Y., & Chen, L. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.729978 · Cited by 62 · see citing works
Qu, R., Xu, G., Ding, C., Jia, W., & Sun, M. (2020) · IEEE Access · doi:10.1109/access.2020.2991845 · Cited by 59 · see citing works
Sharma, H., Drukker, L., Chatelain, P., Droste, R., Papageorghiou, A. T., & Noble, J. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101973 · Cited by 48 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“AI algorithms for the automated detection of various standard planes in US video scans have been reported by Płotka et al. [25], Chen et al. [28], and Baumgartner et al. [15].”
BackgroundMethodArroyo, J., Marini, T. J., Saavedra, A. C., Toscano, M., Baran, T. M., Drennan, K., Dozier, A., Zhao, Y. T., Egoavil, M., Tamayo, L., et al. (2022) · PLOS ONE · doi:10.1371/journal.pone.0262107 · Cited by 40 · see citing works
Maraci, M. A., Yaqub, M., Craik, R., Beriwal, S., Self, A., von Dadelszen, P., Papageorghiou, A., & Noble, J. A. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.1.014501 · Cited by 36 · see citing works
Drukker, L., Sharma, H., Karim, J. N., Droste, R., Noble, J. A., & Papageorghiou, A. T. (2022) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.24975 · Cited by 36 · see citing works
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
Yang, X., Dou, H., Huang, R., Xue, W., Huang, Y., Qian, J., Zhang, Y., Luo, H., Guo, H., Wang, T., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3069663 · Cited by 32 · see citing works
Hybrid descriptor for placental maturity grading
Lei, B., Jiang, F., Zhou, F., Ni, D., Yao, Y., Chen, S., & Wang, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08489-x · Cited by 3 · see citing works
Self-citation
- Authors
- Chen, H., Wu, L., Dou, Q., Qin, J., Li, S., Cheng, J.-Z., Ni, D., & Heng, P.-A.
- Affiliations
- Chinese University of Hong Kong (HK)
- Shenzhen University (CN)
- Hong Kong Polytechnic University (HK)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2017-06
- Volume
- 47
- Issue
- 6
- Pages
- 1576-1586
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2598799153
- Cited by
- 164
- References
- 65
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 26 · 2023: 24 · 2022: 25 · 2021: 19 · 2020: 19 · 2019: 16 · 2018: 6 · 2017: 6
- FWCI
- 5.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Basic Research Program of China, 973 Program (2015CB351706)
- National Natural Science Foundation of China (61571304, 61501305, 61233012)
- Research Grants Council of Hong Kong Special Administrative Region (CUHK 14202514)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- fetal anatomy, prenatal ultrasound, convolutional recurrent neural network, multi-task learning, representation learning, limited training data, fetal biometry
- MeSH terms
- Algorithms, Female, Fetus, Humans, Image Processing, Computer-Assisted, Pregnancy, Video Recording, 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