Standard Plane Identification in Fetal Brain Ultrasound Scans Using a Differential Convolutional Neural Network
Open accessAbstract
Ultrasound scanning has become a highly recommended examination in prenatal diagnosis in many countries. The accurate identification of fetal brain ultrasound scans is crucial to accurate head measurement and brain lesion detection, such as the measurement of the biparietal diameter and the detection of hydrocephalus. In recent years, deep learning has made great progress in the field of image processing. However, there are two difficulties in the identification of fetal brain ultrasound standard planes (FBSPs). First, since the fetal brain tissue is not mature, the fetal brain tissue features are not easy to be detected. Second, because of the expensive collection costs, the amount of labeled image data is limited, which can cause over-fitting and decrease the identification precision. In this study, we proposed a differential convolutional neural network (differential-CNN) to automatically identify six fetal brain standard planes (FBSPs) from the non-standard planes. In this differential-CNN framework, the additional differential feature maps were derived from the feature maps in the original CNN using differential operators. The derivation process did not increase the number of convolution layers and parameters. Moreover, the differential convolution maps have the large advantage of analyzing the directional pattern of pixels and their neighborhoods using additional variation calculations. Therefore, the differential convolution maps would result in good identification performance and cost no extra computational burden. To test the performance of these algorithms, we constructed a dataset consisting of 30,000 2D ultrasound images from 155 fetal subjects ranging from 16 to 34 weeks. The experimental results showed that this method achieved an accuracy of 92.93%. Our work shows that the differential-CNN can be used to facilitate the implementation of the automated identification of FBSPs.
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Two studies focused on deep learning systems for the detection of fetal intracranial planes from 2D ultrasound.
“Two studies focused on deep learning systems for the detection of fetal intracranial planes from 2D ultrasound.92,93”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [93]
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Cites
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
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
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
Acoustic Modeling Using Deep Belief Networks
Mohamed, A., Dahl, G. E., & Hinton, G. E. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2109382 · Cited by 1,731 · see citing works
Convolutional neural networks at constrained time cost
He, K., & Sun, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299173 · Cited by 1,558 · see citing works
Automated breast cancer detection and classification using ultrasound images: A survey
Cheng, H.-D., Shan, J., Ju, W., Guo, Y., & Zhang, L. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.05.012 · Cited by 729 · see citing works
Computer-aided detection and classification of microcalcifications in mammograms: a survey
Cheng, H.-D., Cai, X., Chen, X.-W., Hu, L.-M., & Lou, X. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00192-4 · Cited by 601 · see citing works
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 · 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
Learn Convolutional Neural Network for Face Anti-SpoofingPreprint
Yang, J., Lei, Z., & Li, S. Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5601 · Cited by 378 · see citing works
Recognition of Multiple-Food Images by Detecting Candidate Regions
Matsuda, Y., 甫足, 創., & Yanai, . (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.157 · Cited by 369 · see citing works
Dubourg, C., Bendavid, C., Pasquier, L., Henry, C., Odent, S., & David, V. (2007) · Orphanet Journal of Rare Diseases · doi:10.1186/1750-1172-2-8 · Cited by 355 · see citing works
Chen, C. W., Luo, J., & Parker, K. J. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.730379 · Cited by 294 · 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
Beyond spatial pyramids: Receptive field learning for pooled image features
Jia, Y., Huang, C., & Darrell, T. (2012) · 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.2012.6248076 · Cited by 239 · see citing works
A generalized confusion matrix for assessing area estimates from remotely sensed data
Lewis, H. G., & Brown, M. (2001) · International Journal of Remote Sensing · doi:10.1080/01431160152558332 · Cited by 238 · see citing works
Improved method of handwritten digit recognition tested on MNIST database
Kussul, E. M., & Baydyk, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.03.008 · Cited by 236 · see citing works
Differential convolutional neural network
Sarıgül, M., Ozyildirim, B. M., & Avcı, M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.025 · Cited by 186 · see citing works
Ultrasound 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 · see citing works
A support vector machine with a hybrid kernel and minimal vapnik-chervonenkis dimension
Tan, Y., & Wang, J. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.1269664 · Cited by 141 · see citing works
Yu, 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
Liver fibrosis grade classification with B-mode ultrasound
Yeh, W., Huang, S., & Li, P. (2003) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(03)01010-x · Cited by 104 · see citing works
Chen, Z., He, N., Huang, Y., Qin, W. T., Liu, X., & Li, L. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.08.004 · Cited by 100 · see citing works
A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses
Nguyen, G. N., Sơn, L. H., Ashour, A. S., & Dey, N. (2017) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-017-0691-7 · Cited by 99 · see citing works
Learning-based prediction of gestational age from ultrasound images of the fetal brain
Namburete, A. I. L., Stebbing, R. V., Kemp, B., Yaqub, M., Papageorghiou, A. T., & Alison Noble, J. (2015) · Medical Image Analysis · doi:10.1016/j.media.2014.12.006 · Cited by 97 · 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
Yang, J., Xie, F., Fan, H., Jiang, Z., & Liu, J. (2018) · IEEE Access · doi:10.1109/access.2018.2877587 · Cited by 89 · see citing works
A survey of GPU-based medical image computing techniques.
Shi, L., Liu, W., Zhang, H., Xie, Y., & Wang, D. (2012) · PubMed Central · doi:10.3978/j.issn.2223-4292.2012.08.02 · Cited by 75 · see citing works
Lei, B., Huang, S., Li, R., Bian, C., Li, H., Chou, Y., & Cheng, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.043 · Cited by 74 · 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
Breast Tumor Detection in Ultrasound Images Using Deep Learning
Cao, Z., Duan, L., Yang, G., Yue, T., Chen, Q., Fu, H., & Xu, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67434-6_14 · Cited by 59 · 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
A Deep Learning Design for Improving Topology Coherence in Blood Vessel Segmentation
Araújo, R. J., Cardoso, J. S., & Oliveira, H. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_11 · Cited by 50 · see citing works
Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector
Lei, B., Tan, E.-L., Chen, S., Zhuo, L., Li, S., Ni, D., & Wang, T. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121838 · Cited by 43 · see citing works
Kumar, A., Sridar, P., Quinton, A. E., Kumar, R. K., Feng, D. D., Nanan, R. K. H., & Kim, J. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493385 · Cited by 40 · see citing works
Bian, C., Lee, R., Chou, Y., & Cheng, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_30 · Cited by 35 · see citing works
Modulation of auditory processing by cortico-cortical feed-forward and feedback projections
Tang, J., & Suga, N. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0802961105 · Cited by 29 · see citing works
Computerized detection and recognition of follicles in ovarian ultrasound images: a review
Potočnik, B., Cigale, B., & Zazula, D. (2012) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-012-0956-y · Cited by 18 · see citing works
Biparietal Thinning: Accidental Death by a Fall from Standing Height
Mann, R. W., Kobayashi, M., & Schiller, A. L. (2017) · Journal of Forensic Sciences · doi:10.1111/1556-4029.13425 · Cited by 18 · see citing works
Daoud, M. I., Atallah, A. A., Awwad, F. R., & Al-Najar, M. S. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493367 · Cited by 16 · see citing works
Automatic measurement of biparietal diameter with a portable ultrasound device
Khan, N. H., Tegnander, E., Dreier, J. M., Eik‐Nes, S. H., Torp, H. G., & Kiss, G. H. (2014) · IEEE International Ultrasonics Symposium · doi:10.1109/ultsym.2014.0114 · Cited by 10 · see citing works
Moshfegh, A., Javadzadegan, A., Mohammadi, M., Ravipudi, L., Cheng, S., & Martins, R. N. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.03.008 · Cited by 8 · see citing works
[Diagnostic value of power Doppler ultrasonography for Sirenomelia Seguence in prenatal].
Yan, X.-Y., Yang, T., Hong, L., Tian, Y., & Yang, F. (2011) · PubMed · Cited by 3 · see citing works
De-correlating CNN Features for Generative Classification
Desai, C., Eledath, J., Sawhney, H., & Bansal, M. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.63 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- G. E. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-R. Mohamed, N. Jaitly, and A. Senior, ''Deep neural networks for acoustic model- ing in speech recognition,'' IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82-97, Oct. 2012.
- GUIZHI XU (Member, IEEE) was born in 1962. She received the Ph.D. degree from the School of Electrical Engineering, Hebei University of Tech- nology, Tianjin, China, in 2002.
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 209 · see citing works
Fetal health classification from cardiotocographic data using machine learning
Mehbodniya, A., Lazer, A. J. P., Webber, J. L., Sharma, D. K., Santhosh, J., Kousalya, K., Singh, P., Rajan, R., Pandya, S., & Sengan, S. (2021) · Expert Systems · doi:10.1111/exsy.12899 · Cited by 117 · 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
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
“92,93 developed a system to identify five specific common brain abnormalities including hydrocephalus, ventriculomegaly, Blake's pouch cyst, Dandy Walker malformation and cerebellar vermis hypoplasia.”
BackgroundApplication and Progress of Artificial Intelligence in Fetal Ultrasound
Xiao, S., Zhang, J., Zhu, Y. C., Zhang, Z., Cao, H., Xie, M., & Zhang, L. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12093298 · Cited by 74 · 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
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
Logarithmic Learning Differential Convolutional Neural Network
Magombe, Y., Sarıgül, M., & Avcı, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106114 · Cited by 53 · see citing works
Shahrivar, R. Y., Karami, F., & Karami, E. (2023) · Biomimetics · doi:10.3390/biomimetics8070519 · Cited by 57 · see citing works
Ghabri, H., Alqahtani, M. S., Othman, S. B., Al‐Rasheed, A., Abbas, M. A. E., Almubarak, H. A., Sakli, H., & Abdelkarim, M. N. (2023) · Scientific Reports · doi:10.1038/s41598-023-44689-0 · Cited by 43 · 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
Alzubaidi, M. S., Agus, M., Alyafei, K. A., Al-Thelaya, K., Shah, U., Abd-Alrazaq, A. A., Anbar, M., Makhlouf, M. A., & Househ, M. S. (2022) · iScience · doi:10.1016/j.isci.2022.104713 · Cited by 40 · see citing works
Jain, E., Kaushik, P., Kukreja, V., Sakshi, S., Dogra, A., & Goyal, B. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056360199250227053012 · Cited by 29 · see citing works
Convolutional Neural Network to Detect and Measure Fetal Skull Circumference in Ultrasound Imaging
Skeika, E. L., da Luz, M. R., Fernandes, B., Siqueira, H. V., & de Andrade, M. L. S. C. (2020) · IEEE Access · doi:10.1109/access.2020.3032376 · Cited by 28 · see citing works
Zhu, F., Liu, M., Wang, F., Qiu, D., Li, R., & Dai, C. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021387 · Cited by 28 · see citing works
van den Noort, F., Manzini, C., van der Vaart, C. H., van Limbeek, M. A. J., Slump, C. H., & Grob, A. T. M. (2022) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.24810 · Cited by 25 · see citing works
Rauf, F., Khan, M. A., Bashir, A. K., Jabeen, K., Hamza, A., Alzahrani, A. I., Alalwan, N., & Masood, A. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1330218 · Cited by 24 · see citing works
Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion
Wang, X., Liu, Z., Du, Y., Diao, Y., Liu, P., Lv, G., & Zhang, H. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/6656942 · Cited by 23 · see citing works
An improvement for the automatic classification method for ultrasound images used on CNN
Avazov, K., Abdusalomov, A. B., Mukhiddinov, M., Baratov, N., Makhmudov, F., & Cho, Y. I. (2021) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s0219691321500545 · Cited by 23 · see citing works
Qiu, R., Zhou, M., Bai, J., Lu, Y., & Wang, H. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03111-1 · Cited by 23 · see citing works
Zhen, C., Wang, H., Cheng, J., Yang, X., Chen, C., Hu, X., Zhang, Y., Cao, Y. W., Ni, D., Huang, W., et al. (2023) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2023.05.005 · Cited by 23 · see citing works
Zeng, P., Liu, S., He, S., Zheng, Q., Wu, J., Liu, Y., Liu, Y., & Lyu, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107069 · Cited by 21 · see citing works
An ensemble deep learning framework for foetal plane identification
Thomas, S., & Harikumar, S. (2024) · International Journal of Information Technology · doi:10.1007/s41870-023-01709-6 · Cited by 20 · see citing works
Tang, J., Han, J., Xie, B., Xue, J., Zhou, H., Jiang, Y., Hu, L., Chen, C., Zhang, K., Zhu, F., et al. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20032377 · Cited by 13 · see citing works
Turkan, M., Dandıl, E., Urfalı, F. E., & Korkmaz, M. (2025) · IEEE Access · doi:10.1109/access.2025.3553548 · Cited by 14 · see citing works
A comprehensive review on fetal health surveillance using ultrasound imagery
Mushtaq, G., & Veningston, K. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06644-8 · Cited by 14 · see citing works
Cao, X., Li, B., Zhou, Y., Cao, Y. W., Yang, X. Y., Hu, X., Chen, K., Zhu, S., Lin, H.-L., Wang, T., et al. (2025) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-025-07485-4 · Cited by 9 · see citing works
Gao, Z., Tan, G., Wang, C., Lin, J., Pu, B., Li, S., & Li, K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108885 · Cited by 9 · see citing works
Wang, L., Fatemi, M., & Alizad, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110312 · Cited by 8 · see citing works
Alasmawi, H., Bricker, L., & Yaqub, M. K. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.01.010 · Cited by 7 · see citing works
Srivastava, S., Vidyarthi, A., & Jain, S. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09148-x · Cited by 6 · see citing works
Towards Standard Plane Prediction of Fetal Head Ultrasound with Domain Adaption
Men, Q., Zhao, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230542 · Cited by 6 · see citing works
HCN : Hybrid Capsule Network for Fetal Plane Classification in Ultrasound Images
Tanwar, S. K., Choudhary, P., Priyanka, P., & Agrawal, T. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23149 · Cited by 5 · see citing works
Shamya, A., Vijikala, V., & Dhas, A. S. (2024) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics63763.2024.10859516 · Cited by 5 · see citing works
Fetal Health Prediction Using Machine Learning Approach
Chandana, C., Neha, P. N., Nisarga, S. M., Thanvi, P., & Balarengadurai, C. (2023) · Cognitive science and technology · doi:10.1007/978-981-19-8086-2_88 · Cited by 5 · see citing works
Standard Plane Classification of Fetal Brain Ultrasound Images
Shanavas, J., & Kanjana, G. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-4626-6_41 · Cited by 5 · see citing works
FBStrNet: Automatic Fetal Brain Structure Detection in Early Pregnancy Ultrasound Images
Lin, Y., Liu, S., Liu, Z., Fan, Y., Liu, P., & Guo, X. (2025) · Sensors · doi:10.3390/s25165034 · Cited by 4 · see citing works
Vetriselvi, D., & Thenmozhi, R. (2025) · Journal of Clinical Ultrasound · doi:10.1002/jcu.23924 · Cited by 4 · see citing works
Pratap, T., Dhulipalla, V. R., & Kokil, P. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107918 · Cited by 3 · see citing works
Suha, S. A., & Shahriyar, R. (2025) · Neuroscience Informatics · doi:10.1016/j.neuri.2025.100241 · Cited by 3 · see citing works
Vatanparast, A., Fateh, M., & Mashayekhi, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108586 · Cited by 2 · see citing works
Dey, S. K., Howlader, A., Haider, M. S., Saha, T., Setu, D. M., Islam, T., Siddiqi, U. R., & Rahman, M. M. (2025) · Journal of Perinatal Medicine · doi:10.1515/jpm-2025-0249 · Cited by 3 · see citing works
Abdillahi, M. A., Baker, M. R., Doğru, A., & Buyrukoğlu, S. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01753-7 · Cited by 2 · see citing works
Fetal MoAt Net: a light-weight deep learning model for fetal diagnostic plane classification
Mushtaq, G., & Veningston, K. (2025) · International Journal of Computers and Applications · doi:10.1080/1206212x.2025.2543550 · Cited by 2 · see citing works
“Deep learning” for healthcare: Opportunities, threats, and challenges
Kabir, R., Sivasubramanian, M., Hitch, G., Hakkim, S., Kainesie, J., Vinnakota, D., Mahmud, I., Apu, E. H., Syed, H. Z., & Parsa, A. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00017-5 · Cited by 2 · see citing works
A Review of AI-Based Approaches for Suspected Fetal Abnormalities
Ansari, N. J. I., Edinburg, M. M., & NIKITHA, N. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-61471-2_30 · Cited by 1 · see citing works
Zhao, L., Xu, Y., Xu, J., Ding, W., Yang, J., Zhou, H., Du, Y., Hu, B., Zhang, L., & Wang, Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110687 · Cited by 1 · see citing works
FBSPM-Net: Automatic recognition of fetal brain ultrasound images in early pregnancy
Lin, Y., Guo, X., & Liu, P. (2025) · International Conference on Health Big Data · doi:10.1145/3733006.3733020 · Cited by 1 · see citing works
Salman, H., Pradat‐Peyre, J., Guéhis, S., Chrara, N., Zaki, C., & Nasser, A. (2026) · Neuroscience Informatics · doi:10.1016/j.neuri.2026.100272 · Cited by 1 · see citing works
SEG-FAUSP: Anatomical Structure Segmentation of the Standard Sections of Fetal Abdominal Ultrasounds
Chen, J., Liu, P., Yang, X., Wang, X., Wu, X., Liu, Z., & Liu, S. (2026) · Bioengineering · doi:10.3390/bioengineering13040403 · Cited by 1 · see citing works
Yang, J., Yuan, J., & Li, W. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114765 · Cited by 1 · see citing works
Liang, H., Zhang, Y., Zhu, X., Huang, Y., Du, X., Liang, S., Xu, J., Zhang, Y., Sheng, C., Liu, Y., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104201 · Cited by 1 · see citing works
Ghabri, H., Fathallah, W., Sakli, H., & Abdelkarim, M. N. (2023) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista59065.2023.10310548 · Cited by 1 · see citing works
Automatic analysis of transperineal ultrasound images
den Noort, F. V. L.-V. (2022) · doi:10.3990/1.9789464192292
Vatanparast, A., Fateh, M., Mashayekhi, H., & Ferdowsi, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-28201-4
Deep Learning Based Fetal Health Classification: A Systematic Literature Review
Bajaj, D., Agarwal, A., Narang, D., & Kumari, D. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27160-0_27
Zhang, D., & Zhang, B. (2026) · Physica Medica · doi:10.1016/j.ejmp.2026.105806
Use of Artificial Intelligence in Fetal Brain Ultrasound Examination: A review
Yuanjie, C., Guo, M. C., Li, Z., Zhang, J. S., Wu, Y., Wu, Q., & Sun, M. L. (2026) · Advanced ultrasound in diagnosis and therapy · doi:10.26599/audt.2026.250061
Self-citationSuha, S. A., Islam, M. N., & Shahriyar, R. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-026-00897-y
- Authors
- Qu, R., Xu, G., Ding, C., Jia, W., & Sun, M.
- Affiliations
- Hebei University of Technology (CN)
- University of Pittsburgh (US)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 83821-83830
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3021993456
- Cited by
- 59
- References
- 50
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 8 · 2023: 16 · 2022: 5 · 2021: 5 · 2020: 1
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology
- National Natural Science Foundation of China (51677053, 51737003, 51807048)
- 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
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Keywords
- fetal brain ultrasound, differential operators, fetal brain tissue, biparietal diameter, prenatal diagnosis, limited labeled data
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