Improving Fetal Head Contour Detection by Object Localisation with Deep Learning
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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
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 56,035 · 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
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
A systematic review of the ultrasound estimation of fetal weight
Dudley, N. J. (2005) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.1751 · Cited by 537 · see citing works
Automated measurement of fetal head circumference using 2D ultrasound images
van den Heuvel, T. L. A., de Bruijn, D., de Korte, C. L., & Ginneken, B. V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0200412 · Cited by 280 · see citing works
Fetal Size and Dating: Charts Recommended for Clinical Obstetric Practice
Loughna, P., Chitty, L. S., Evans, T., & Chudleigh, T. (2009) · Ultrasound · doi:10.1179/174313409x448543 · Cited by 209 · 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
Intra‐ and interobserver variability in fetal ultrasound measurements
Sarris, I., Ioannou, C., Chamberlain, P., Ohuma, E., Roseman, F., Hoch, L., Altman, D. G., Papageorghiou, A. T., & for the International Fetal and Newborn Growth Consortium for the 21 Century (INTERGROWTH‐21) (2012) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.10082 · Cited by 195 · see citing works
Rueda, S., Fathima, S., Knight, C. L., Yaqub, M. K., Papageorghiou, A. T., Rahmatullah, B., Foi, A., Maggioni, M., Pepe, A., Tohka, J., et al. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2276943 · Cited by 177 · see citing works
How accurate is fetal biometry in the assessment of fetal age?
Chervenak, F. A., Skupski, D. W., Romero, R. J., Myers, M. K., Smith‐Levitin, M., Rosenwaks, Z., & Thaler, H. T. (1998) · American Journal of Obstetrics and Gynecology · doi:10.1016/s0002-9378(98)70477-6 · Cited by 163 · see citing works
Cascaded Fully Convolutional Networks for automatic prenatal ultrasound image segmentation
Wu, L., Yang, X., Li, S., Wang, T., Heng, P., & Ni, D. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950607 · Cited by 126 · see citing works
Li, J., Wang, Y., Lei, B., Cheng, J.-Z., Qin, J., Wang, T., Li, S., & Ni, D. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2703890 · Cited by 120 · see citing works
Looney, P., Stevenson, G. N., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Collins, S. L. (2018) · JCI Insight · doi:10.1172/jci.insight.120178 · Cited by 111 · see citing works
Lü, W., Tan, J., & Floyd, R. C. (2005) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2005.04.002 · Cited by 106 · see citing works
Segmentation of fetal ultrasound images
Jardim, S., & Figueiredo, M. A. T. (2005) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2004.11.003 · Cited by 84 · 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
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
Fetal ultrasound image segmentation system and its use in fetal weight estimation
Yu, J., Wang, Y., & Chen, P. (2008) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-008-0407-y · Cited by 40 · see citing works
Finding the most accurate method to measure head circumference for fetal weight estimation
Schmidt, U., Temerinac, D., Bildstein, K., Tuschy, B., Mayer, J., Sütterlin, M., Siemer, J., & Kehl, S. (2014) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2014.03.047 · Cited by 34 · see citing works
An appraisal of ultrasound fetal biometry in the first trimester
Burd, I. D. (2010) · Reports in Medical Imaging · doi:10.2147/rmi.s9119 · Cited by 8 · see citing works
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A review on deep-learning algorithms for fetal ultrasound-image analysis
Fiorentino, M. C., Villani, F. P., Cosmo, M. D., Frontoni, E., & Moccia, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102629 · Cited by 209 · see citing works
Zeng, Y., Tsui, P.-H., Wu, W., Zhou, Z., & Wu, S. (2021) · Journal of Digital Imaging · doi:10.1007/s10278-020-00410-5 · Cited by 113 · 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
A regression framework to head-circumference delineation from US fetal images
Fiorentino, M. C., Moccia, S., Capparuccini, M., Giamberini, S., & Frontoni, E. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105771 · Cited by 54 · see citing works
Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation
Chenarlogh, V. A., Oghli, M. G., Shabanzadeh, A., Sirjani, N., Akhavan, A., Shiri, I., Arabi, H., Taheri, M. S., & Tarzamni, M. K. (2022) · Ultrasonic Imaging · doi:10.1177/01617346211069882 · Cited by 49 · 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
Alzubaidi, M., Agus, M., Shah, U., Makhlouf, M., Alyafei, K., & Househ, M. (2022) · Diagnostics · doi:10.3390/diagnostics12092229 · 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
Deep Learning Strategies for Ultrasound in Pregnancy
Diniz, P. H. B., Yin, Y., & Collins, S. (2020) · EMJ Reproductive Health · doi:10.33590/emjreprohealth/20-00100 · Cited by 32 · see citing works
Automatic linear measurements of the fetal brain on MRI with deep neural networks
Avisdris, N., Yehuda, B., Ben-Zvi, O., Link‐Sourani, D., Ben‐Sira, L., Miller, E., Zharkov, E., Bashat, D. B., & Joskowicz, L. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02436-8 · Cited by 32 · see citing works
Moccia, S., Fiorentino, M. C., & Frontoni, E. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02430-0 · Cited by 30 · see citing works
Anatomically Guided Cross-Domain Repair and Screening for Ultrasound Fetal Biometry
Gao, J. Y., Lao, Q., Liu, P., Yi, H., Kang, Q., Jiang, Z., Wu, X., Li, K., Chen, Y., & Zhang, L. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3298096 · Cited by 24 · see citing works
Alzahrani, T., Al‐Nuaimy, W., & Al‐Bander, B. (2021) · Computation · doi:10.3390/computation9050054 · Cited by 23 · see citing works
Self-citationWang, X., Wang, W., & Cai, X. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105515 · Cited by 23 · see citing works
Hekal, A. A., Amer, H. M., Moustafa, H. E.-D., & Elnakib, A. A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107434 · Cited by 19 · see citing works
Dubey, G., Srivastava, S., Jayswal, A. K., Saraswat, M., Singh, P., & Memoria, M. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00908-8 · Cited by 19 · see citing works
BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes
Avisdris, N., Leo Joskowicz, B., Dromey, B., David, A. L., Peebles, D., Stoyanov, D. V., Bashat, D. B., & Bano, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16440-8_27 · Cited by 14 · see citing works
An improved semantic segmentation with region proposal network for cardiac defect interpretation
Nurmaini, S., Tama, B. A., Rachmatullah, M. N., Darmawahyuni, A., Sapitri, A. I., Firdaus, F., & Tutuko, B. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07217-1 · Cited by 14 · see citing works
Ciceri, T., Squarcina, L., Pigoni, A., Ferro, A., Montano, F., Bertoldo, A., Persico, N., Boito, S., Triulzi, F. M., Conte, G., et al. (2023) · Neuroinformatics · doi:10.1007/s12021-023-09635-5 · Cited by 13 · see citing works
Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review
Kareem, F. Q., & Abdulazeez, A. M. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4621288 · Cited by 13 · see citing works
Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review
admin, A., & Abdulazeez, A. M. (2021) · Fusion Practice and Applications · doi:10.54216/fpa.030102 · Cited by 13 · see citing works
Srivastava, S., Vidyarthi, A., & Jain, S. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104725 · Cited by 12 · 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
The End-to-End Fetal Head Circumference Detection and Estimation in Ultrasound Images
Zhao, L., Li, N., Tan, G., Chen, J., Li, S., & Duan, M. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3227037 · Cited by 12 · 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
Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasound
Bastiaansen, W., Rousian, M., Steegers‐Theunissen, R. P. M., Niessen, W. J., Koning, A. H. J., & Klein, S. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-cb15 · Cited by 8 · see citing works
Ellipse guided multi-task network for fetal head circumference measurement
Wang, J., Fang, Z., Yao, S., & Yang, F. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104535 · Cited by 8 · see citing works
Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review
Bastiaansen, W., Klein, S., Koning, A. H. J., Niessen, W. J., Steegers‐Theunissen, R. P. M., & Rousian, M. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104466 · Cited by 5 · see citing works
Ahmed, P., Yusuf, M. S. U., Sarker, S., Chowdhury, A., Bhowmik, A., & Rahman, F. (2024) · International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) · doi:10.1109/ic-etite58242.2024.10493596 · Cited by 4 · see citing works
Automatic Fetus Head Segmentation In Ultrasound Images By Attention Based Encoder Decoder Network
Bhalla, P., Sunkaria, R. K., Kamboj, A., & Bedi, A. K. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9579657 · Cited by 4 · see citing works
Torres, H. R., Oliveira, B., Morais, P. R., Fritze, A., Birdir, C., Rüdiger, M., Fonseca, J. C., & Vilaça, J. L. (2022) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2611150 · Cited by 4 · see citing works
Semi-automatic Contour “Gist” Creation for Museum Painting Tactile Exploration
Dao, S. D., Truong, N.-T., Pissaloux, E. E., Romeo, K., & Djoussouf, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08648-9_31 · Cited by 3 · see citing works
Fetal Head Detection in 2D Ultrasound Images using Deep Learning
Ketheesan, T., & Janani, T. (2020) · Cited by 3 · see citing works
Detection of Fetal Cardiac Chamber Three Vessel Trachea View using Deep Learning
Sapitri, A. I., Nurmaini, S., Rini, D. P., Rachmatullah, M. N., Darmawahyuni, A., & Gusendi, A. (2022) · Proceeding of the Electrical Engineering Computer Science and Informatics · doi:10.23919/eecsi56542.2022.9946528 · Cited by 3 · see citing works
A multicentre benchmark dataset for comprehensive landmark-based fetal ultrasound biometry
Vece, C. D., Mao, Z., Avisdris, N., Dromey, B., Napolitano, R., Bashat, D. B., Vasconcelos, F., Stoyanov, D. V., Joskowicz, L., & Bano, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-47854-3 · Cited by 2 · see citing works
Segmentation of Fetal 2D Images with Deep Learning: A Review
CEDRI, P. R., Rehman, M. A. U., & CEDRI, G. I. (2022) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme55909.2022.9988625 · Cited by 1 · see citing works
Towards Real-time Tracking of Fetal Head in 3D during Childbirth
Marcadent, S., Hêches, J., Moser, L., Favre, J., Desseauve, D., & Thiran, J. (2023) · IEEE International Ultrasonics Symposium · doi:10.1109/ius51837.2023.10308353 · Cited by 1 · see citing works
MFA-FHS: multiscale feature based self attention network for fetal head segmentation
Kamboj, A., Bhalla, P., & Sunkaria, R. K. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20824-z · Cited by 1 · see citing works
Detection and Classification of Pregnancy StateUsing Deep Learning Technique
Mohammed, H. A., Hussein, E. M., & Sharif, M. M. (2021) · Omdurman Islamic University Journal · doi:10.52981/oiuj.v17i2.1819
Systematic Review: Recent Advancements in Deep Learning Techniques for Facial Feature Recognition
Adapa, S., & Enireddy, V. (2025) · Data & Metadata · doi:10.56294/dm2025658
Li, Z., Chen, L., Lu, L., Ding, Y., & Hao, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0033-8_37
Mohan, M. H., Seeboruth, K., Rana, M. E., Umar, U. S., Mohan, T., & Safavi, S. (2025) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic64892.2025.11158010
Constructive learning: a high-performance framework for fetal head circumference estimation
Shojaedini, S. V., & Momenian, M. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08293-z
Sarcheshmeh, F. M., Karimi, A., & Rahmati, N. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02026-0
- Authors
- Al-Bander, B., Alzahrani, T., Alzahrani, S., Williams, B. M., & Zheng, Y.
- Affiliations
- University of Liverpool (GB)
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 142-150
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W3003030062
- Cited by
- 44
- References
- 21
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 3 · 2023: 8 · 2022: 13 · 2021: 8 · 2020: 3
- FWCI
- 2.47
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Communications in computer and information science (publishedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- fetal head segmentation, ultrasound images, object localization, fully convolutional network, ellipse fitting
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