Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images
Open accessAbstract
Objective : Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. Approach : This paper proposes a method for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed convolutional neural network (CNN) and U-Net for each process. These machine learning techniques take clinicians’ decisions, anatomical structures, and the characteristics of ultrasound images into account. The proposed method is divided into three steps: initial abdominal circumference (AC) estimation, AC measurement, and plane acceptance checking. Main results : A CNN is used to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein), and a Hough transform is used to obtain an initial estimate of the AC. These data are applied to other CNNs to estimate the spine position and bone regions. Then, the obtained information is used to determine the final AC. After determining the AC, a U-Net and a classification CNN are used to check whether the image is suitable for AC measurement. Finally, the efficacy of the proposed method is validated by clinical data. Significance : Our method achieved a Dice similarity metric of for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Two 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 [65]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
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
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
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
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
Obstetrics: Normal and Problem Pregnancies
Schewitz, L. J. (1998) · JAMA · doi:10.1001/jama.279.11.886 · Cited by 212 · 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
Performance of 36 Different Weight Estimation Formulae in Fetuses with Macrosomia
Hoopmann, M., Abele, H., Wagner, N., Wallwiener, D., & Kagan, K. O. (2010) · Fetal Diagnosis and Therapy · doi:10.1159/000299475 · Cited by 96 · see citing works
Intrauterine growth retardation.
Vandenbosche, R. C., & Kirchner, J. T. (1998) · PubMed · Cited by 94 · 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
Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images
Jang, J., Park, Y., Kim, B., Lee, S. M., Kwon, J.-Y., & Seo, J. K. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2776116 · Cited by 63 · see citing works
Self-citationAutomatic 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
Sovio, U., Moraitis, A. A., Wong, H. S., & Smith, G. C. S. (2017) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.17491 · Cited by 47 · see citing works
Fetal Abdominal Contour Extraction and Measurement in Ultrasound Images
Yu, J., Wang, Y., Chen, P., & Shen, Y. (2007) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2007.06.026 · Cited by 44 · 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
Improved segmentation of ultrasound images for fetal biometry, using morphological operators
Shrimali, V., Anand, R. S., & Kumar, V. (2009) · doi:10.1109/iembs.2009.5334470 · Cited by 39 · see citing works
Ultrasonographic Fetal Weight Estimation: Should Macrosomia-Specific Formulas Be Utilized?
Porter, B. W., Neely, C. L., Szychowski, J. M., & Owen, J. (2015) · American Journal of Perinatology · doi:10.1055/s-0035-1545664 · Cited by 20 · 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
Canavan, T. P., & Hill, L. M. (2014) · Journal of Clinical Ultrasound · doi:10.1002/jcu.22230 · Cited by 6 · 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
A Fully Automated Method for 3D Individual Tooth Identification and Segmentation in Dental CBCT
Jang, T. J., Kim, K. C., Cho, H. C., & Seo, J. K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3086072 · Cited by 164 · see citing works
Self-citationMachine Learning in Fetal Cardiology: What to Expect
Garcia-Canadilla, P., Sanchez-Martinez, S., Crispi, F., & Bijnens, B. (2020) · Fetal Diagnosis and Therapy · doi:10.1159/000505021 · Cited by 123 · see citing works
Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging
Komatsu, M., Sakai, A., Dozen, A., Shozu, K., Yasutomi, S., Machino, H., Asada, K., Kaneko, S., & Hamamoto, R. (2021) · Biomedicines · doi:10.3390/biomedicines9070720 · Cited by 119 · 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
Zhang, Z., & Han, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2977962 · Cited by 83 · 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.”
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
A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT
Gupta, K. D., Sharma, D. K., Ahmed, S., Gupta, H., Gupta, D. K., & Hsu, C. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10555-1 · Cited by 67 · see citing works
Artificial Intelligence in Prenatal Ultrasound Diagnosis
He, F., Wang, Y., Xiu, Y., Zhang, Y., & Chen, L. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.729978 · Cited by 62 · see citing works
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
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
Ultrasound Bone Segmentation: A Scoping Review of Techniques and Validation Practices
Pandey, P. U., Quader, N., Guy, P., Garbi, R., & Hodgson, A. J. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2019.12.014 · Cited by 47 · 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
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-citationDevelopment of a Machine Learning Model for Sonographic Assessment of Gestational Age
Lee, C., Willis, A., Chen, C., Sieniek, M., Watters, A., Stetson, B., Uddin, A., Wong, J., Pilgrim, R., Chou, K., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.48685 · Cited by 42 · 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
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
Hussein, I. J., Burhanuddin, M. A., Mohammed, M. A., Benameur, N., Maashi, M. S., & Maashi, M. S. (2021) · Expert Systems · doi:10.1111/exsy.12789 · Cited by 35 · 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
Machine 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
Automated Fetal Head Classification and Segmentation Using Ultrasound Video
Rasheed, K., Junejo, F., Malik, A. I., & Saqib, M. (2021) · IEEE Access · doi:10.1109/access.2021.3131518 · Cited by 30 · see citing works
Lee, S., Kang, M., Byeon, K., Lee, S. E., Lee, I. H., Kim, Y. A., Kang, S., & Park, J. T. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00625-8 · Cited by 28 · see citing works
Classification of First Trimester Ultrasound Images Using Deep Convolutional Neural Network
Singh, R. K., Mahmud, M., & Yovera, L. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-82269-9_8 · Cited by 26 · see citing works
DeepGA for automatically estimating fetal gestational age through ultrasound imaging
Dan, T., Chen, X., He, M., Guo, H., He, X., Chen, J., Xian, J., Hu, Y., Zhang, B., Wang, N., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102453 · Cited by 25 · see citing works
Bashir, Z., Lin, M., Feragen, A., Mikolaj, K. W., Taksøe‐Vester, C., Christensen, A. N., Svendsen, M. B. S., Fabricius, M. H., Andreasen, L. A., Nielsen, M., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-86536-4 · Cited by 26 · see citing works
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
Tan, J., Au, A., Meng, Q., FinesilverSmith, S. L., Simpson, J. M., Rueckert, D., Razavi, R., Day, T. G., Lloyd, D. F. A., & Kainz, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60334-2_24 · Cited by 21 · see citing works
Explainable AI in Deep Learning Based Classification of Fetal Ultrasound Image Planes
Harikumar, A., Surendran, S., & Gargi, S. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.03.291 · Cited by 23 · see citing works
Deepika, P., Suresh, R., & Pabitha, P. (2020) · Computational Intelligence · doi:10.1111/coin.12394 · Cited by 19 · see citing works
RetractedRanger, B. J., Bradburn, E., Chen, Q., Kim, M., Noble, J. A., & Papageorghiou, A. T. (2023) · Gates Open Research · doi:10.12688/gatesopenres.15088.1 · Cited by 14 · see citing works
Zheng, Z., Qiu, S., Yue, X., Wang, J., & Hou, J. (2024) · Journal of Nuclear Materials · doi:10.1016/j.jnucmat.2024.155117 · Cited by 13 · 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-citationLei, T., Feng, J. L., Lin, M. F., Xie, B. H., Zhou, Q., Wang, N., Zheng, Q., Yang, Y. D., Guo, H., & Xie, H. (2023) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.15167 · Cited by 9 · see citing works
Deep Learning Approach for Automatic Wrist Fracture Detection Using Ultrasound Bone Probability Maps
Hareendranathan, A. R., Tripathi, A., Panicker, M. R., Zhang, J., Boora, N., & Jaremko, J. L. (2023) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-023-01608-8 · 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
Ambroise-Grandjean, G., Hossu, G., Banasiak, C., Ciofolo, C., Raynaud, C., Rouet, L., Morel, O., & Beaumont, M. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-031777 · Cited by 8 · 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
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
Artificial Intelligence in Prenatal Ultrasound: Clinical Application and Prospect
Guo, M. W., Li, M. S., Yu, M. X., Wen, B. H., Yuan, M. Y., & Yang, M. X. (2023) · Advanced ultrasound in diagnosis and therapy · doi:10.37015/audt.2023.230024 · 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
The Classification and Segmentation of Fetal Anatomies Ultrasound Image: A Survey
Song, C., Gao, T., Wang, H., Sudirman, S., Zhang, W., & Zhu, H. (2020) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2021.3616 · Cited by 6 · see citing works
Sutton, S., Mahmud, M., Singh, R. P., & Yovera, L. (2022) · Analogical and Inductive Inference · doi:10.1007/978-3-031-24801-6_17 · Cited by 3 · see citing works
Artificial Intelligence for Fetal Ultrasound
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-citationAhmed, 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
Enhancing ectopic pregnancy detection: a machine learning approach using ultrasound imaging
Suresh, L. R., & Lakshmanan, S. K. (2025) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2025.2479118 · Cited by 3 · see citing works
Ultrasound-based horizontal ranging in the localization of fetal conus medullaris
Liu, X., Li, P., Yang, Y., & Tian, C. (2023) · Technology and Health Care · doi:10.3233/thc-230332 · Cited by 3 · see citing works
Zukünftige Bildanalyse mit Hilfe automatisierter Algorithmen
Weichert, J., Rody, A., & Gembicki, M. (2020) · Der Gynäkologe · doi:10.1007/s00129-020-04563-w · Cited by 3 · see citing works
Fiorentino, M. C., Federici, L., Camera, A. P. L., & Caiani, E. G. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109178 · Cited by 2 · see citing works
Banumathi, S., & Aruna, R. (2024) · Australian Journal of Electrical & Electronics Engineering · doi:10.1080/1448837x.2024.2429920 · Cited by 2 · see citing works
Yuan, Z., Xu, T., Yu, C., Ye, X., & Zhang, J. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23031 · Cited by 1 · see citing works
Jiji, G. W., Rajesh, A., & Muthuraj, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19885-3 · Cited by 1 · see citing works
HAPAN: Support Tool for Practicing Regional Anesthesia in Peripheral Nerves
Hernández-Muriel, J. A., Mejía-Hernández, J. C., Echeverry-Correa, J. D., Orozco, A. A., & Peña, D. A. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-19591-5_14 · Cited by 1 · see citing works
A Novel CNN Framework for Early-Stage Detection of Blindness in Diabetic Patients
Gupta, K. D., Bhatnagar, A., Gupta, A., & Sharma, T. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-2719-5_10
Stellenwert der KI im pränatalen sonographischen Screening
Ritgen, J. (2022) · Die Gynäkologie · doi:10.1007/s00129-022-04995-6
Standardization of Reference Values for Fetal Gastric Size in Normal Gesta
Mousa, W. A. E., ElGendy, R. S. A. M., & Ella, T. F. A. (2023) · The Egyptian Journal of Hospital Medicine · doi:10.21608/ejhm.2023.287827
Artificial Intelligence and the second trimester of pregnancy: A literature survey
Belciug, S. (2023) · Intelligent Decision Technologies · doi:10.3233/idt-230077
Analysis of Amniotic fluid in fetal assessment using Biophysical Profile Images
Tamilselvi, R., Maraikkayar, S. M. S. M. A., Beham, M. P., & Kannan, S. (2022) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3571600.3571637
Deep Learning Techniques for Medical Image Segmentation and Object Recognition
Kim, K. C., Jang, T. J., & Seo, J. K. (2023) · Mathematics in industry · doi:10.1007/978-981-99-1839-3_2
Self-citationRajesh, P., Thanjaivadivel, M., Arulkumaran, G., & Jagannathan, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4854264/v1
Determining Fetal Orientations From Blind Sweep Ultrasound Video
Wiśniewski, J. M., Christensen, A. N., Ngo, M. L., Tolsgaard, M. G., & Wong, C. K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-95918-9_18
- Authors
- Kim, B., Kim, K. C., Park, Y., Kwon, J.-Y., Jang, J., & Seo, J. K.
- Affiliations
- Yonsei University (KR)
- National Institute for Mathematical Sciences (KR)
- To whom correspondence should be addressed.
- Corresponding author
- Seo, Jin Keun
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2018-10-01
- Volume
- 39
- Issue
- 10
- Pages
- 105007
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2890559952
- Cited by
- 62
- References
- 26
- Citations by year
- 2025: 8 · 2024: 8 · 2023: 13 · 2022: 12 · 2021: 12 · 2020: 7 · 2019: 2
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Funders
- National Research Foundation of Korea
- 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 › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- fetal abdominal circumference, fetal biometry, convolutional neural networks, U-Net, Hough transform, image classification, prenatal ultrasound
- MeSH terms
- Abdomen, Machine Learning, Biometry, Female, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Pregnancy, Ultrasonography, Prenatal
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review