The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks
Abstract
The quality of ultrasound (US) images for the obstetric examination is crucial for accurate biometric measurement. However, manual quality control is a labor intensive process and often impractical in a clinical setting. To improve the efficiency of examination and alleviate the measurement error caused by improper US scanning operation and slice selection, a computerized fetal US image quality assessment (FUIQA) scheme is proposed to assist the implementation of US image quality control in the clinical obstetric examination. The proposed FUIQA is realized with two deep convolutional neural network models, which are denoted as L-CNN and C-CNN, respectively. The L-CNN aims to find the region of interest (ROI) of the fetal abdominal region in the US image. Based on the ROI found by the L-CNN, the C-CNN evaluates the image quality by assessing the goodness of depiction for the key structures of stomach bubble and umbilical vein. To further boost the performance of the L-CNN, we augment the input sources of the neural network with the local phase features along with the original US data. It will be shown that the heterogeneous input sources will help to improve the performance of the L-CNN. The performance of the proposed FUIQA is compared with the subjective image quality evaluation results from three medical doctors. With comprehensive experiments, it will be illustrated that the computerized assessment with our FUIQA scheme can be comparable to the subjective ratings from medical doctors.
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“Wu et al. proposed a computerized quality assessment scheme for quality control of US images by identifying the ROI in fetal abdominal images [175].”
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“and the development of toolkits for education [175].”
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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
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
(2008) · Journal of Machine Learning Research · Cited by 33,051
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
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
(2011) · Cited by 6,993
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
4 million neonatal deaths: When? Where? Why?
Lawn, J. E., Cousens, S., & Zupan, J. (2005) · The Lancet · doi:10.1016/s0140-6736(05)71048-5 · Cited by 3,670
Image Features From Phase Congruency
Kovesi, P. D. (1995) · UWA Profiles and Research Repository (University of Western Australia) · Cited by 1,113
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968
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Qin, Y., Dang, L., Ren, F., Li, Z., Duan, L., Wang, H., & Han, J. (2026) · Sensors · doi:10.3390/s26072081 · Cited by 1
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
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Santacruz-Yaah, X. E. (2022) · Science Insights · doi:10.15354/si.22.re069 · Cited by 1
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
Guangzhu, X., Qian, Y., Yang, W., Rong, L., Jun, Z., & Lei, B. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220347 · Cited by 1
Benjamin, J. G., Asokan, M., Alhosani, A., Alasmawi, H., Diehl, W., Bricker, L., Nandakumar, K., & Yaqub, M. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44521-7_7 · Cited by 1
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Qi, X., Zhang, Y., Xue, L., Chen, W., Zhang, G., & Hou, L. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105604 · Cited by 1
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Alfuraydi, A., Alquraini, H., & Alotaibi, M. (2024) · Saudi Journal of Radiology · doi:10.55038/sjr.v2i2.167 · Cited by 2
Automated Segmentation of Breast Cancer Focal Lesions on Ultrasound Images
Валерьевич, П. Д., Александрович, Е. И., Kolchev, A. А., Kliouchkin, I. V., Олеговна, П. О., Saad, Z., Daou, A., & Abuzenar, E. M. (2025) · Sensors · doi:10.3390/s25051593 · Cited by 1
Skeika, E. L., de Andrade, M. L. S. C., & Siqueira, H. V. (2019) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2019.8324 · Cited by 1
Opinion-unaware blind picture quality measurement using deep encoder–decoder architecture
Zhou, W., Lin, X., Zhou, X., Lei, J., Yu, L., & Luo, T. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102834 · Cited by 1
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Veena, S., & Aravindhar, D. J. (2021) · Medico-Legal Update · doi:10.37506/mlu.v21i2.2821 · Cited by 1
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Shaikh, S. (2022) · Springer eBooks · doi:10.1007/978-981-16-9535-3_24 · Cited by 1
Yu, M., Gao, Y., Jin, J., Ruan, J., He, L., Shen, Q., & Gong, X. (2022) · Research Square · doi:10.21203/rs.3.rs-1213399/v1 · Cited by 1
Anwar, S., Ajmal, F., Tasneef, F., Muneer, A., Mughees, A., Sajid, W., Ahmed, T., Shams, R. M. A. A., Sajawal, R. M. B. K., Babar, A., et al. (2021) · Journal of Health, Medicine and Nursing · doi:10.7176/jhmn/93-04
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Slimani, S., Slimani, S., Hounka, S., Mahmoudi, A., Laoudiyi, D., Saadi, H., Bouzyiane, A., Lamrissi, A., Jalal, M., Bouhya, S., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2239392/v1
A COMPREHENSIVE STUDY ON HEALTH MONITORING SYSTEMS FOR PREGNANT WOMEN AND FETUS
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets36849
Accuracy in classification of liver lesion ultrasound using artificial intelligence
Tangruangkiat, S., Krisanachinda, A., & Auethavekiat, S. (2022) · doi:10.58837/chula.the.2022.243
Хмара, Т. В., KOVAL, O. A., Zamorskii, І. І., Гарвасюк, О., & Kryvchanska, M. I. (2024) · Archives of the Balkan Medical Union · doi:10.31688/abmu.2024.59.1.01
Aop-Sam: Segment Anything Model in Intrapartum Ultrasound Image AnalysisPreprint
Zhou, Z., Lu, Y., Bai, J., Campello, V. M., Feng, F., & Lekadir, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4885247
US Scanning Technologies and AI
Ali, S., & Ahmed, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-59531-8_5
Tian, H., Liu, Y., Au, F. W.-F., & Lin, G. (2024) · medRxiv · doi:10.1101/2024.09.05.24313076
Midyanti, H. I., Sukmayadi, Y., Sella, F., & Midyanti, D. M. (2024) · SHS Web of Conferences · doi:10.1051/shsconf/202419703002
From out-of-distribution detection to quality control
Lambert, B., Forbes, F., & Michel Dojat, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00014-6 · Cited by 1
A Comparative Exploration of ML Classifiers in Recognition of Ovarian Cancer
Sharma, S., Nachappa, M. N., & Kapse, V. M. (2024) · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) · doi:10.1109/icscai61790.2024.10866241
Chen, K., Gu, W., Li, Y., Liu, X., Shen, J., & Tang, D. (2026) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae71d3
Which Image Quality Measure is Optimal for Ultrasound Imaging?
Nowicki, F., Abed‐Meraim, K., Savéry, D., & Weber, R. (2025) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta66025.2025.11222031
Zheng, Q., Li, L., Huang, Y., Lyu, G., & Zeng, P. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109260 · Cited by 1
Structure-based curriculum learning ultrasound gallbladder image classification network
Mu, X., Yang, S., Zhuo, J., Li, Y., Wang, J., Peng, C., & Wang, X. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113652
Hu, X., Wang, J., Cao, Y., Hu, W., Li, Y., Liu, Z., & Li, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357070
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Shojaedini, S. V., & Momenian, M. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08293-z
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Wang, Z., Zhao, B., Yao, L., Lei, L., Lv, F., Li, B., & Hu, Y. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3674247
Awe, O. D., Oliveira, A. P. D. M. E., Charles, C. M., Oladimeji, K. E., Torezzan, C., & Pacagnella, R. C. (2026) · Research Square · doi:10.21203/rs.3.rs-8651301/v1 · Cited by 1
Pu, B., Yang, J., Lv, X., Dong, X., Zhao, L., Li, S., Li, K., & Li, X. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02682-2
Jiang, Y., Tang, J., Zhu, F., Zeng, Y., Wang, W., Liu, Y., Wang, H., & Lu, L. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110323
Defining Robust Ultrasound Quality Metrics via an Ultrasound Foundation Model
Huang, Z., Li, B., Ma, C., Liu, T., Zhai, Y., Xu, H., Guo, Y., Li, Z., & Wang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_12
Explicit force-based control strategies for compliant robot-assisted ultrasound imaging
Piedra, A., Jeffrey, R. B., & Khatib, O. (2026) · The International Journal of Robotics Research · doi:10.1177/02783649261441635
Enywaku, A., & Asiku, R. A. (2026) · medRxiv · doi:10.64898/2026.05.04.26352354
Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
He, D., Wang, H. H., & Yaqub, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13654-1_11 · Cited by 4
Vard, A., Aissa, A., Rossant, F., Isabelle Bloch, A., Castro‐Farías, D., Chabriat, H., & Pâques, M. (2026) · Translational Vision Science & Technology · doi:10.1167/tvst.15.10.8
Metadata
- Authors
- Wu, L., Cheng, J.-Z., Li, S., Lei, B., Wang, T., & Ni, D.
- Affiliations
- Shenzhen University (CN)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2017-05
- Volume
- 47
- Issue
- 5
- Pages
- 1336-1349
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2593232761
- Cited by
- 242
- References
- 41
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 27 · 2023: 38 · 2022: 34 · 2021: 42 · 2020: 23 · 2019: 29 · 2018: 10 · 2017: 3
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81571758, 61571304, 61501305)
- National Key Research and Development Program of China (2016YFC0104703)
- Shenzhen Basic Research Project (JCYJ20150525092940982, JCYJ20140509172609164)
- Natural Science Foundation of SZU (2016089)
- Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (MJUKF201711)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- prenatal ultrasound, image quality assessment, deep convolutional neural networks, region of interest detection, umbilical vein, image quality control, obstetric examination, biometric measurements
- MeSH terms
- Female, Fetus, Humans, Image Processing, Computer-Assisted, Pregnancy, Quality Control, Ultrasonography, Prenatal, Neural Networks, Computer
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