Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images
Open accessAbstract
Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.
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A limitation of AI applications for fetal biometry is reduced algorithm accuracy with higher GA with large fetuses.
“higher GA with large fetuses [21]”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [21]
The abstract (p. 1) says the proposed automated abdominal-circumference method could not correctly estimate measurements in some cases of oversized fetuses, and the Discussion (p. 8) identifies oversized fetuses as a limitation. The paper does not establish that accuracy decreases with higher gestational age, nor does this result apply to AI applications for fetal biometry generally. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTwo studies assessed automated measurement of the abdominal circumference only.
“two studies assessed automated measurement of AC only.64,65”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [64]
AI usage in standard plane acquisition has the benefit of incorporation of clinical aspects into image interpretation.
“the incorporation of clinical aspects into image interpretation [21,23,26]”
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2014) · Cited by 33,419 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · 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
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works
Estimation of fetal weight with the use of head, body, and femur measurements—A prospective study
Hadlock, F. P., Harrist, R. B., Sharman, R. S., Deter, R. L., & Park, S. K. (1985) · American Journal of Obstetrics and Gynecology · doi:10.1016/0002-9378(85)90298-4 · Cited by 2,682 · see citing works
Speckle reducing anisotropic diffusion
Yu, Y., & Acton, S. T. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.804276 · Cited by 1,854 · see citing works
A new curve detection method: Randomized Hough transform (RHT)
Xu, L., Oja, E., & Kultanen, P. (1990) · Pattern Recognition Letters · doi:10.1016/0167-8655(90)90042-z · Cited by 1,027 · 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
ULTRASONIC MEASUREMENT OF FETAL ABDOMEN CIRCUMFERENCE IN THE ESTIMATION OF FETAL WEIGHT
Campbell, S. C., & Wilkin, D. (1975) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/j.1471-0528.1975.tb00708.x · Cited by 541 · 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
Randomized Hough Transform: Improved ellipse detection with comparison
McLaughlin, R. A. (1998) · Pattern Recognition Letters · doi:10.1016/s0167-8655(98)00010-5 · Cited by 287 · see citing works
A new efficient ellipse detection method
Xie, Y., & Ji, Q. (2003) · doi:10.1109/icpr.2002.1048464 · Cited by 223 · 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
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
Edge-guided boundary delineation in prostate ultrasound images
Pathak, D. S., Haynor, D. R., & Kim, Y. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.897813 · Cited by 170 · 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
A method to detect and characterize ellipses using the Hough transform
Bennett, N., Burridge, R., & Saito, N. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.777377 · Cited by 75 · 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
Ravishankar, H., Prabhu, S. M., Vaidya, V., & Singhal, N. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493382 · Cited by 58 · see citing works
Automatic fetal head measurements from sonographic images
Chalana, V., Winter, T. C., Cyr, D. R., Haynor, D. R., & Kim, Y. (1996) · Academic Radiology · doi:10.1016/s1076-6332(96)80187-5 · Cited by 56 · see citing works
Does the Use of Automated Fetal Biometry Improve Clinical Work Flow Efficiency?
Espinoza, J., Good, S., Russell, E., & Lee, W. (2013) · Journal of Ultrasound in Medicine · doi:10.7863/jum.2013.32.5.847 · Cited by 53 · 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
Does the Use of Automated Fetal Biometry Improve Clinical Work Flow Efficiency?
Espinoza, J., Good, S., Russell, E., et al. (2013) · Journal of Ultrasound in Medicine · doi:10.7863/ultra.32.5.847 · Cited by 31 · see citing works
Wang, W., Qin, J., Zhu, L., Ni, D., Chui, Y.-P., & Heng, P. (2014) · Bio-Medical Materials and Engineering · doi:10.3233/bme-130928 · Cited by 20 · see citing works
Detection of Intrauterine Growth Retardation Using Fetal Abdominal Circumference
Nithya, J., & Madheswaran, M. (2009) · International Conference on Computer Technology and Development · doi:10.1109/icctd.2009.213 · Cited by 11 · see citing works
Automated scoring of fetal abdomen ultrasound scan-planes for biometry
Kumar, A. M. C., & Shriram, K. S. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7164007 · Cited by 7 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence in ultrasound
Shen, Y., Chen, L., Yue, W., & Xu, H. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109717 · Cited by 220 · 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
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
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: An Update and Future Applications
Chen, Z., Liu, Z., Du, M., & Wang, Z. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.733468 · Cited by 77 · see citing works
Application 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
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
“Five studies assessed deep learning for automated measurement of the FL only and two studies assessed automated measurement of AC only.(64,65) There F I GUR E 1 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) flow diagram.”
BackgroundArtificial 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
Kim, B., Kim, K. C., Park, Y., Kwon, J.-Y., Jang, J., & Seo, J. K. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aae255 · Cited by 62 · see citing works
Self-citationLi, Z., Li, W., Wei, Y., Gui, G., Zhang, R., Liu, H., Chen, Y., & Jiang, Y. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101882 · Cited by 59 · 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
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
“The limitations of AI applications for fetal biometry were found to be reduced algo-rithm accuracy in poor quality images due to high maternal BMI [22,23], low contrast of anatomical structures [36], and higher GA with large fetuses [21].”
BackgroundMethodInfluentialLocalization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks
Sobhaninia, Z., Emami, A., Karimi, N., & Samavi, S. (2020) · International Computer Conference, Computer Society of Iran (CSICC) · doi:10.1109/csicc49403.2020.9050094 · Cited by 44 · see citing works
Automated ultrasound assessment of amniotic fluid index using deep learning
Cho, H. C., Sun, S., Min Hyun, C., Kwon, J.-Y., Kim, B., Park, Y., & Seo, J. K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101951 · Cited by 43 · see citing works
Self-citationFetalNet: 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
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
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
RetractedMachine Learning in Point of Care Ultrasound
Sonko, M. L., Arnold, T., & Kuznetsov, I. A. (2022) · POCUS Journal · doi:10.24908/pocus.v7ikidney.15345 · Cited by 31 · see citing works
Bai, J., Zhou, Z., Ou, Z., Koehler, G., Stock, R., Maier-Hein, K. H., Elbatel, M., Martí, R., Li, X., Qiu, Y., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103353 · Cited by 27 · 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
Shiwlani, A. (2024) · American Journal of Biomedical Science & Research · doi:10.34297/ajbsr.2024.22.002970 · Cited by 20 · 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
Deepika, P., Suresh, R., & Pabitha, P. (2020) · Computational Intelligence · doi:10.1111/coin.12394 · Cited by 19 · see citing works
RetractedA system-on-chip solution for deep learning-based automatic fetal biometric measurement
Cho, H., Kim, D., Chang, S., Kang, J., & Yoo, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121482 · Cited by 19 · see citing works
Precise segmentation of fetal head in ultrasound images using improved U‐Net model
Nagabotu, V., & Namburu, A. (2023) · ETRI Journal · doi:10.4218/etrij.2023-0057 · Cited by 18 · see citing works
Gao, Z., Tian, Z., Pu, B., Li, S., & Li, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107399 · Cited by 17 · see citing works
Ultrasound tissue classification: a review
Shan, C., Tan, T., Han, J., & Huang, D. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09920-8 · Cited by 15 · 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
Complementary Network for Accurate Amniotic Fluid Segmentation From Ultrasound Images
Sun, S., Kwon, J., Park, Y., Cho, H. C., Hyun, C. M., & Seo, J. K. (2021) · IEEE Access · doi:10.1109/access.2021.3098844 · Cited by 14 · see citing works
Self-citationZhao, L., Tan, G., Wu, Q., Pu, B., Ren, H., Li, S., & Li, K. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3392531 · Cited by 13 · see citing works
Han, X., Yu, J., Yang, X., Chen, C., Zhou, H., Qiu, C., Cao, Y., Zhang, T., Peng, M., Zhu, G., et al. (2024) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-024-06336-y · Cited by 12 · see citing works
Li, J., Gao, Z., Wang, C., Pu, B., & Li, K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129290 · Cited by 11 · see citing works
Bouachba, A., Neves, J. D. J., Royer, E., Bartin, R., Salomon, L. J., Grévent, D., & Gorincour, G. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.29172 · 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
Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning
Xu, K.-L., You, K., Zhu, B.-Q., Feng, M., Feng, D.-W., & Yang, C. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2024.3374966 · Cited by 8 · see citing works
Automatic Measurement of Fetal Head Circumference from 2-Dimensional Ultrasound
Aji, C. P., Fatoni, M. H., & Sardjono, T. A. (2019) · International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) · doi:10.1109/cenim48368.2019.8973258 · Cited by 8 · see citing works
Amniotic Fluids Classification Using Combination of Rules-Based and Random Forest Algorithm
Ayu, P. D. W., Hartati, S. T., Musdholifah, A., & Nurdiati, D. S. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-7334-4_20 · Cited by 8 · see citing works
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Cho, H. C., Sun, S., Park, S. W., Kwon, J., & Seo, J. K. (2023) · Mathematics in industry · doi:10.1007/978-981-99-1839-3_5 · Cited by 6 · see citing works
Self-citationHarish, S., & Reeja, S. R. (2024) · International Conference on Emerging Systems and Intelligent Computing (ESIC) · doi:10.1109/esic60604.2024.10481665 · Cited by 6 · see citing works
Automatic Identification of Fetal Abdominal Planes from Ultrasound Images Based on Deep Learning
Ciobanu, Ș. G., Enache, I.-A., Iovoaica-Rămescu, C., Berbecaru, E. I. A., Vochin, A., Băluță, I. D., Istrate-Ofiţeru, A.-M., Comănescu, C. M., Nagy, R. D., Şerbănescu, M.-S., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01409-6 · Cited by 6 · see citing works
Computer-Aided Monitoring of Fetus Health From Ultrasound Images: A Review
Ataş, D., & Yılmaz, Y. B. (2022) · Acta Infologica · doi:10.26650/acin.1099106 · Cited by 6 · see citing works
Multi-Derivational Parsing of Vague Languages— The New Paradigm of Syntactic Pattern Recognition
Flasiński, M., Jurek, J., & Peszek, T. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367245 · Cited by 6 · see citing works
ScanAhead: Simplifying standard plane acquisition of fetal head ultrasound
Men, Q., Zhao, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103614 · Cited by 5 · see citing works
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Liu, Z., Wang, J., Lyu, G., Song, H., Yu, W., Liu, P., Fan, Y., & Wan, Y. (2025) · Sensors · doi:10.3390/s25030633 · Cited by 4 · see citing works
Kumari, J. S. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/87932020 · Cited by 4 · see citing works
Gao, Z., Ding, Y., Zhu, N., Zhang, H., Wang, C., Li, S., & Li, K. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3503679 · Cited by 4 · see citing works
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Govindarajan, M. P. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.055536 · Cited by 2 · see citing works
Tan, Y., Wen, H., Peng, G., Wen, H., Wen, X., Jiang, Y., Fan, J., Yuan, Y., Luo, D., & Li, S. (2024) · Ultrasound International Open · doi:10.1055/a-2337-0078 · Cited by 2 · see citing works
An Intelligent Fetal Prognostic System using Machine Learning Techniques
Abishek, S., Mathialagan, A., A, H. O. S. S., Goyal, M., & Mannan, J. M. (2024) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi60426.2024.10503455 · Cited by 1 · see citing works
AGE-US: Automated Gestational Age Estimation Based on Fetal Ultrasound Images
Parga, C. D., Nuñez‐Garcia, M., Carreira, M. J., Bernardino, G., & Vila-Blanco, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99568-2_7 · Cited by 1 · see citing works
MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Xu, C., Wei, J., Chen, H., Liang, P., Liang, B., Tan, Y., & Wei, X. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-3483-8_4 · Cited by 1 · see citing works
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Aji, C. P., Sardjono, T. A., & Fatoni, M. H. (2020) · Jurnal Teknik ITS · doi:10.12962/j23373539.v9i1.45679 · Cited by 1 · see citing works
Liu, J., Tan, Y., Wang, C., Li, K., Tan, G., & Liu, C. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3539391 · Cited by 1 · see citing works
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Belciug, S. (2023) · Intelligent Decision Technologies · doi:10.3233/idt-230077
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Kim, K. C., Jang, T. J., & Seo, J. K. (2023) · Mathematics in industry · doi:10.1007/978-981-99-1839-3_2
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Sweety, K. N., Haque, N., Chowdhury, N., Begum, R., Khan, L. N., Sultana, R., & Shewly, N. R. (2023) · Journal of National Institute of Neurosciences Bangladesh · doi:10.3329/jninb.v9i1.65360
Gadekar, V. P., Damaraju, N., Xavier, A., Thakur, S. B., Vijayram, R., Desiraju, B. K., Misra, S., Bhatnagar, S., Wadhwa, N., Natchu, U. C. M., et al. (2024) · The Lancet Regional Health - Southeast Asia · doi:10.1016/j.lansea.2024.100362
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Sakariya, A., Chaudhary, J., Patel, B., & Barad, J. (2026) · Cybernetics and Information Technologies · doi:10.2478/cait-2026-0031
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- Authors
- Jang, J., Park, Y., Kim, B., Lee, S. M., Kwon, J.-Y., & Seo, J. K.
- Affiliations
- National Institute for Mathematical Sciences (KR)
- Yonsei University (KR)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2018-09
- Volume
- 22
- Issue
- 5
- Pages
- 1512-1520
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2766759990
- Cited by
- 63
- References
- 27
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 17 · 2023: 11 · 2022: 6 · 2021: 10 · 2020: 5 · 2019: 1 · 2018: 2
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Institute for Mathematical Sciences
- Korean Government (A21300000)
- National Research Foundation of Korea (2015R1A5A1009350, 2017R1A2B20005661)
- 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 › Prenatal Screening and Diagnostics
- Keywords
- fetal abdominal circumference, fetal biometry, convolutional neural networks, Hough transform, shadowing artifacts
- MeSH terms
- Abdomen, Female, Fetus, Humans, Image Interpretation, Computer-Assisted, Pregnancy, 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 ›
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