SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
Open accessAbstract
Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.
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AI usage in standard plane acquisition has the benefit of real-time applicability.
“the possibility of real-time applicability [15,25,32,33]”
Conventional assessments of fetal lung maturity rely on invasive amniocentesis and surface-level ultrasound scoring, achieving moderate predictive performance (AUC ≈ 0.82) but lacking dynamic integration of placental function.
“Conventional assessments of fetal lung maturity rely on invasive amniocentesis and surface-level ultrasound scoring, achieving moderate predictive performance (AUC ≈ 0.82) but lacking dynamic integration of placental function (23, 24).”
From Placenta-pulmonary coupling-guided multimodal AI for fetal lung maturity staging and individualized glucocorticoid therapy (2026) · cites this paper as [23]
The Introduction discusses routine 2D ultrasound screening of fetal anatomy, and the Experiments and Results report performance for detecting scan planes—not predicting fetal lung maturity. Searches for amniocentesis, lung-maturity scoring, AUC ≈ 0.82, and placental-function integration found no passage addressing the attributed claim. Page numbers are unavailable in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measuredBaumgartner et al. and Chen et al. accomplished detection of 13 fetal standard views and the fetal abdominal, face, or four-chamber standard plane in 2D US images through transferred deep models.
“Baumgartner et al. [117,118] and Chen et al. [78,119] accomplished the detection of 13 fetal standard views (e.g., kidneys, brain, abdominal, spine, femur, and cardiac plane) and the fetal abdominal (or face and four-chamber view) standard plane in 2D US images through the transferred deep models, respectively.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [118]
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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 · 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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988 · see citing works
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447 · see citing works
An Image Inpainting Technique Based on the Fast Marching Method
Telea, A. (2004) · Journal of Graphics Tools · doi:10.1080/10867651.2004.10487596 · Cited by 1,421 · see citing works
Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhou, Z., & Liu, X. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.17 · Cited by 1,354 · see citing works
Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan
Salomon, L. J., Alfirevic, Z., Berghella, V., Bilardo, C., Hernandez‐Andrade, E., Johnsen, S. L., Kalache, K., Leung, K. ‐., Malinger, G., Munoz, H., et al. (2011) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.8831 · Cited by 1,006 · see citing works
Is object localization for free? - Weakly-supervised learning with convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298668 · Cited by 886 · see citing works
From image-level to pixel-level labeling with Convolutional Networks
Pinheiro, P. O., & Collobert, R. (2015) · 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.2015.7298780 · Cited by 691 · 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
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
Lin, M., Chen, Q., & Yan, S. (2013) · arXiv · doi:10.48550/arxiv.1312.4400 · Cited by 252 · 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
Fully Convolutional Neural Networks for Crowd SegmentationPreprint
Kang, K., & Wang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4464 · Cited by 126 · see citing works
Disparities in the prenatal detection of critical congenital heart disease
Hill, G. D., Block, J. R., Tanem, J., & Frommelt, M. A. (2015) · Prenatal Diagnosis · doi:10.1002/pd.4622 · Cited by 119 · see citing works
Baumgartner, C. F., Kamnitsas, K., Matthew, J., Smith, S., Kainz, B., & Rueckert, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_24 · Cited by 100 · see citing works
Self-citationWeakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting
Ren, W., Huang, K., Tao, D., & Tan, T. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2456908 · Cited by 79 · 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
Automated annotation and quantitative description of ultrasound videos of the fetal heart
Bridge, C. P., Ioannou, C., & Noble, J. A. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.11.006 · Cited by 64 · see citing works
Yaqub, M., Kelly, B., Papageorghiou, A. T., & Noble, J. A. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24574-4_82 · Cited by 56 · 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
Images thresholding using ISODATA technique with gamma distribution
El‐Zaart, A. (2010) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661810010037 · Cited by 46 · see citing works
Volumetric (3D) imaging reduces inter‐ and intraobserver variation of fetal biometry measurements
Chan, L. W., Fung, T. Y., Leung, T. Y., Sahota, D. S., & Lau, T. K. (2009) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.6321 · Cited by 40 · see citing works
Selective 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
Searching 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
Object localisation in fetal ultrasound images using invariant features
Bridge, C. P., & Noble, J. A. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163839 · Cited by 15 · see citing works
Fisher vector encoding for detecting objects of interest in ultrasound videos
Maraci, M. A., Napolitano, R., Papageorghiou, A. T., & Noble, J. A. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163957 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [31]Radiographers S. O., “ Sonographer workforce survey analysis,” The Society Radiographers, Tech. Rep., 2014, pp. 28– 35.
Cited by
Sorted by citations
Attention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014 · see citing works
Self-citationRecent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M. A., Thai, T., Moore, K. N., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,129 · see citing works
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-supervised learning for medical image analysis using image context restoration
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101539 · Cited by 575 · see citing works
Self-citationA Novel Ultrasound Robot With Force/Torque Measurement and Control for Safe and Efficient Scanning
Bao, X., Wang, S., Zheng, L., Housden, R. J., Hajnal, J. V., & Rhode, K. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3239925 · Cited by 432 · see citing works
Deep learning in ultrasound imaging
van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316 · see citing works
Arnaout, R., Curran, L., Zhao, Y., Levine, J. C., Chinn, E., & Moon-Grady, A. J. (2020) · doi:10.1101/2020.06.22.20137786 · Cited by 315 · see citing works
Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., & Gratacós, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-67076-5 · Cited by 240 · see citing works
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 218 · see citing works
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 209 · see citing works
Robotic ultrasound imaging: State-of-the-art and future perspectives
Jiang, Z., Salcudean, S. E., & Navab, N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102878 · Cited by 190 · 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
A Survey on Multimodal Data-Driven Smart Healthcare Systems: Approaches and Applications
Cai, Q., Wang, H. H., Li, Z., & Liu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2941419 · Cited by 165 · see citing works
Becker, A. S., Mueller, M., Stoffel, E., Marcon, M., Ghafoor, S., & Boss, A. (2018) · The British Journal of Radiology · doi:10.1259/bjr.20170576 · Cited by 157 · see citing works
Visual Feature Attribution Using Wasserstein GANs
Baumgartner, C. F., Koch, L. M., Tezcan, K. C., Ang, J. X., & Konukoğlu, E. (2018) · 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.2018.00867 · Cited by 151 · see citing works
Self-citationAn Overview of Systems and Techniques for Autonomous Robotic Ultrasound Acquisitions
Li, K., Xu, Y., & Meng, M. Q. ‐. (2021) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2021.3072190 · Cited by 152 · see citing works
Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning
Komatsu, M., Sakai, A., Komatsu, R., Matsuoka, R., Yasutomi, S., Shozu, K., Dozen, A., Machino, H., Hidaka, H., Arakaki, T., et al. (2021) · Applied Sciences · doi:10.3390/app11010371 · Cited by 142 · 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
Østvik, A., Smistad, E., Aase, S. A., Haugen, B. O., & Løvstakken, L. (2018) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2018.07.024 · Cited by 127 · see citing works
Zhang, J., Liu, M., Wang, L., Chen, S., Yuan, P., Li, J., Shen, S. G.-F., Tang, Z., Chen, K.-C., Xia, J. J., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101621 · Cited by 119 · see citing works
A 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
Use 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
Robot-Assisted Medical Imaging: A Review
Salcudean, S. E., Moradi, H., Black, D., & Navab, N. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3162840 · Cited by 101 · see citing works
Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., & Noble, J. A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.006 · Cited by 95 · see citing works
Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects
Torrents-Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.10.003 · Cited by 97 · see citing works
Dhombres, F., Bonnard, J., Bailly, K., Maurice, P., Papageorghiou, A. T., & Jouannic, J.-M. (2022) · Journal of Medical Internet Research · doi:10.2196/35465 · Cited by 93 · 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
Kim, K. C., Cho, H. C., Jang, T. J., Choi, J. M., & Seo, J. K. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105833 · Cited by 88 · see citing works
Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation
Reynaud, H., Vlontzos, A., Hou, B., Beqiri, A., Leeson, P., & Kainz, B. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87231-1_48 · Cited by 84 · see citing works
Self-citationRecent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
Tenajas, R., Miraut, D., Illana, C., Alonso-González, R., Arias-Valcayo, F., & Herraiz, J. L. (2023) · Applied Sciences · doi:10.3390/app13063693 · Cited by 79 · see citing works
Sinclair, M. D., Baumgartner, C. F., Matthew, J., Bai, W., Martinez, J. C., Li, Y., Smith, S., Knight, C. L., Kainz, B., Hajnal, J. V., et al. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512278 · Cited by 78 · see citing works
Self-citationDeep learning for ultrasound image caption generation based on object detection
Zeng, X., Wen, L., Liu, B., & Qi, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.114 · Cited by 76 · see citing works
Li, K., Wang, J., Xu, Y., Qin, H., Liu, D., Liu, L., & Meng, M. Q. ‐. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561295 · Cited by 76 · see citing works
Xiao, Q., Brown, L. G., Foran, D. J., Nosher, J. L., & Hacihaliloglu, I. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02305-w · Cited by 72 · 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
“Two studies evaluated AI systems to automatically detect 13 standard anatomic planes.(105,106) In the study by Matthews et al.”
MethodEfficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries
Smith, J. W., & Torlak, M. (2022) · IEEE Access · doi:10.1109/access.2022.3145370 · Cited by 72 · see citing works
Sridar, 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
Model-Based and Data-Driven Strategies in Medical Image Computing
Rueckert, D., & Schnabel, J. A. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2943836 · Cited by 73 · see citing works
Self-citationSakai, A., Komatsu, M., Komatsu, R., Matsuoka, R., Yasutomi, S., Dozen, A., Shozu, K., Arakaki, T., Machino, H., Asada, K., et al. (2022) · Biomedicines · doi:10.3390/biomedicines10030551 · Cited by 68 · see citing works
Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models
Krishna, T. B., & Kokil, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122153 · Cited by 67 · 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
A review of image processing methods for fetal head and brain analysis in ultrasound images
Torres, H. R., Morais, P., Oliveira, B., Birdir, C., Rüdiger, M., Fonseca, J. C., & Vilaça, J. L. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106629 · Cited by 64 · see citing works
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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
Artificial Intelligence in Congenital Heart Disease
Jone, P., Gearhart, A. S., Lei, H., Xing, F., Nahar, J. K., López-Jiménez, F. A., Diller, G., Marelli, A. J., Wilson, L., Saidi, A. S., et al. (2022) · JACC Advances · doi:10.1016/j.jacadv.2022.100153 · Cited by 66 · see citing works
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Zhang, B., Liu, H., Luo, H., & Li, K. (2021) · Medicine · doi:10.1097/md.0000000000024427 · Cited by 70 · see citing works
VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation
Bi, Y., Jiang, Z., Gao, Y., Wendler, T., Karlas, A., & Navab, N. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3176112 · Cited by 62 · see citing works
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Li, Y., Khanal, B., Hou, B., Alansary, A., Cerrolaza, J. J., Sinclair, M., Matthew, J., Gupta, C., Knight, C., Kainz, B., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_45 · Cited by 61 · see citing works
Self-citationClinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit
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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
Drukker, L., Sharma, H., Droste, R., Alsharid, M., Chatelain, P., Noble, J. A., & Papageorghiou, A. T. (2021) · Scientific Reports · doi:10.1038/s41598-021-92829-1 · Cited by 59 · see citing works
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Self-citationAutomated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach
Zhao, L., Asis-Cruz, J. D., Feng, X., Wu, Y., Kapse, K. J., Largent, A., Quistorff, J. L., Lopez, C., Wu, D., Qing, K., et al. (2022) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7419 · Cited by 59 · see citing works
Sendra-Balcells, C., Campello, V. M., Torrents-Barrena, J., Ahmed, Y. A., Elattar, M., Ohene-Botwe, B., Nyangulu, P., Stones, W., Ammar, M., Benamer, L. N., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-29490-3 · Cited by 57 · see citing works
Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives
Bi, Y., Jiang, Z., Duelmer, F., Huang, D., & Navab, N. (2024) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-091523-100042 · Cited by 57 · see citing works
Deep learning fetal ultrasound video model match human observers in biometric measurements
Płotka, S., Klasa, A., Lisowska, A., Seliga-Siwecka, J., Lipa, M., Trzciński, T., & Sitek, A. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac4d85 · Cited by 57 · see citing works
Nurmaini, S., Rachmatullah, M. N., Sapitri, A. I., Darmawahyuni, A., Jovandy, A., Firdaus, F., Tutuko, B., & Passarella, R. (2020) · IEEE Access · doi:10.1109/access.2020.3034367 · Cited by 56 · see citing works
Learning to map 2D ultrasound images into 3D space with minimal human annotation
Yeung, P.-H., Aliasi, M., Papageorghiou, A. T., Haak, M., Xie, W., & Namburete, A. I. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101998 · Cited by 55 · see citing works
Self-Supervised Representation Learning for Ultrasound Video
Jiao, J., Droste, R., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020) · 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098666 · Cited by 56 · see citing works
Li, K., Xu, Y., Wang, J., Ni, D., Liu, L., & Meng, M. Q. ‐. (2021) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2021.3127015 · Cited by 53 · see citing works
Borkovkina, S., Camino, A., Janpongsri, W., Šarunic, M. V., & Jian, Y. (2020) · Biomedical Optics Express · doi:10.1364/boe.395279 · Cited by 53 · see citing works
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Meng, Q., Housden, J., Matthew, J., Rueckert, D., Schnabel, J. A., Kainz, B., Sinclair, M., Zimmer, V., Hou, B., Rajchl, M., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2913311 · Cited by 52 · see citing works
Self-citationKrishna, T. B., & Kokil, P. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105283 · Cited by 51 · see citing works
Use of artificial intelligence in obstetrics: Not quite ready for prime time
Sarno, L., Neola, D., Carbone, L., Saccone, G., Carlea, A., Miceli, M., Iorio, G. G., Mappa, I., Rizzo, G., Girolamo, R. D., et al. (2023) · American Journal of Obstetrics & Gynecology MFM · doi:10.1016/j.ajogmf.2022.100792 · Cited by 47 · see citing works
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
Jiao, J., Namburete, A. I. L., Papageorghiou, A. T., & Noble, J. A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3018560 · Cited by 48 · see citing works
Meng, Q., Matthew, J., Zimmer, V. A., Gómez, A., Lloyd, D. F. A., Rueckert, D., & Kainz, B. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3035424 · Cited by 46 · see citing works
Self-citationGenerative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification
Montero, A., Bonet-Carne, E., & Burgos-Artizzu, X. P. (2021) · Sensors · doi:10.3390/s21237975 · Cited by 48 · 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
Oh, K., Yoon, J., & Suk, H.-I. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3197845 · Cited by 48 · see citing works
Yi, J., Kang, H. K., Kwon, J.-H., Kim, K.-S., Park, M. H., Seong, Y. K., Kim, D. W., Ahn, B. E., Ha, K., Lee, J.-Y., et al. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20102 · Cited by 48 · see citing works
VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography
Huang, R., Xie, W., & Noble, J. A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.04.004 · Cited by 47 · see citing works
Cai, Y., Sharma, H., Chatelain, P., & Noble, J. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_98 · Cited by 46 · 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
“Operator-related factors: US in general and obstetric US in particular, are known to require substantial experience and to be extremely training dependent [15].”
BackgroundMethodInfluentialAdaptive feature squeeze network for nuclear cataract classification in AS-OCT image
Zhang, X., Xiao, Z., Higashita, R., Hu, Y., Chen, W., Yuan, J., & Liu, J. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104037 · Cited by 44 · see citing works
Advances in the Application of Artificial Intelligence in Fetal Echocardiography
Zhang, J., Xiao, S., Zhu, Y. C., Zhang, Z., Cao, H., Xie, M., & Zhang, L. (2024) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2023.12.013 · Cited by 61 · see citing works
Deep learning-based real time detection for cardiac objects with fetal ultrasound video
Iriani Sapitri, A., Nurmaini, S., Naufal Rachmatullah, M., Tutuko, B., Darmawahyuni, A., Firdaus, F., Rini, D. P., & Islami, A. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101150 · Cited by 42 · see citing works
Qiao, S., Pang, S., Luo, G., Pan, S., Chen, T., & Lv, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3091579 · Cited by 41 · see citing works
Spatio-temporal visual attention modelling of standard biometry plane-finding navigation
Cai, Y., Droste, R., Sharma, H., Chatelain, P., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101762 · Cited by 41 · see citing works
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
Płotka, S., Wlodarczyk, T. W., Klasa, A., Lipa, M., Sitek, A., & Trzcinski, T. P. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-92310-5_30 · Cited by 41 · see citing works
Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time
Matthew, J., Skelton, E., Day, T. G., Zimmer, V. A., Gomez, A., Wheeler, G., Toussaint, N., Liu, T., Budd, S., Lloyd, K., et al. (2022) · Prenatal Diagnosis · doi:10.1002/pd.6059 · Cited by 41 · see citing works
Self-citationArroyo, 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
Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics
Medjedović, E., Stanojević, M., Jonuzovic-Prosic, S., Ribić, E., Begić, Z., Cerovac, A., & Badnjević, A. (2023) · Technology and Health Care · doi:10.3233/thc-231482 · Cited by 40 · see citing works
Jiang, Z., Bi, Y., Zhou, M., Hu, Y., Burke, M., & Navab, N. (2024) · The International Journal of Robotics Research · doi:10.1177/02783649231223547 · Cited by 40 · see citing works
Ou, Z., Bai, J., Chen, Z., Lu, Y., Wang, H., Long, S., & Chen, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108501 · Cited by 39 · see citing works
RetractedSearching collaborative agents for multi-plane localization in 3D ultrasound
Yang, X., Huang, Y., Huang, R., Dou, H., Li, R., Qian, J., Huang, X., Shi, W., Chen, C., Zhang, Y., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102119 · Cited by 39 · see citing works
Zhang, X., Xiao, Z., Higashita, R., Chen, W., Yuan, J., Fang, J., Hu, Y., & Liu, J. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283218 · Cited by 38 · see citing works
AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes
Bano, S., Dromey, B., Vasconcelos, F., Napolitano, R., David, A. L., Peebles, D., & Stoyanov, D. V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_22 · Cited by 39 · 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
Self-Supervised Contrastive Video-Speech Representation Learning for Ultrasound
Jiao, J., Cai, Y., Alsharid, M., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59716-0_51 · Cited by 38 · 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
SonoEyeNet: Standardized fetal ultrasound plane detection informed by eye tracking
Cai, Y., Sharma, H., Chatelain, P., & Noble, J. A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363851 · Cited by 36 · see citing works
Confident Head Circumference Measurement from Ultrasound with Real-Time Feedback for Sonographers
Budd, S., Sinclair, M. D., Khanal, B., Matthew, J., Lloyd, D. F. A., Gómez, A., Toussaint, N., Robinson, E. C., & Kainz, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9_75 · Cited by 36 · see citing works
Self-citationLi, K., Li, A., Xu, Y., Xiong, H., & Meng, M. Q. ‐. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3246089 · Cited by 36 · see citing works
Spontaneous Preterm Birth Prediction Using Convolutional Neural Networks
Wlodarczyk, T. W., Płotka, S., Rokita, P. S., Sochacki-Wójcicka, N., Wójcicki, J., Lipa, M., & Trzcinski, T. P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60334-2_27 · Cited by 35 · see citing works
Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning
Slimani, S., Hounka, S., Mahmoudi, A., Rehah, T., Laoudiyi, D., Saadi, H., Bouziyane, A., Lamrissi, A., Jalal, M., Bouhya, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42438-5 · Cited by 38 · see citing works
Towards Standardized Acquisition With a Dual-Probe Ultrasound Robot for Fetal Imaging
Housden, R. J., Wang, S., Bao, X., Zheng, J., Skelton, E., Matthew, J., Noh, Y., Eltiraifi, O., Singh, A., Singh, D., et al. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3056033 · Cited by 34 · see citing works
Self-citationWu, L., Bin, D., Liu, X., Hong, W., Chen, L., Gao, K., Sheng, Q., Yu, Y., Zhao, L., & Zhang, Y. (2022) · Frontiers in Pediatrics · doi:10.3389/fped.2021.770182 · Cited by 33 · see citing works
D’Alberti, E., Patey, O. V., Smith, C., Šalović, B., Hernandez-Cruz, N., Noble, J. A., & Papageorghiou, A. T. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103250 · Cited by 35 · see citing works
Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment
Vaze, S., Xie, W., & Namburete, A. I. L. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2961264 · Cited by 33 · see citing works
Selvathi, D., & Chandralekha, R. (2021) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-021-00765-0 · Cited by 33 · see citing works
- Authors
- Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D.
- Affiliations
- Imperial College London (GB)
- Guy's Hospital (GB)
- King's College London (GB)
- Corresponding author
- Baumgartner, Christian F.
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2017-11
- Volume
- 36
- Issue
- 11
- Pages
- 2204-2215
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC6051487
- OpenAlex
- W2735582614
- Cited by
- 416
- References
- 34
- Citations by year
- 2026: 52 · 2025: 48 · 2024: 54 · 2023: 56 · 2022: 55 · 2021: 57 · 2020: 44 · 2019: 28 · 2018: 21 · 2017: 1
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Wellcome Trust IEH Award (102431)
- 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
- real-time detection, weakly supervised learning, image-level labels, convolutional neural networks, bounding box localization
- MeSH terms
- Algorithms, Female, Humans, Image Processing, Computer-Assisted, Pregnancy, Video Recording, Ultrasonography, Prenatal, Neural Networks, Computer
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