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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Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
Deep learning in medical imaging and radiation therapy
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Akkus, Z., Cai, J. C., Boonrod, A., Zeinoddini, A., Weston, A. D., Philbrick, K. A., & Erickson, B. J. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.06.004 · Cited by 342
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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