Cited by 242Image and Video Quality AssessmentMedical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

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FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks

Wu, Lingyun, Cheng, Jie-Zhi, Li, Shengli, Lei, Baiying, Wang, Tianfu, Ni, Dong·2017·IEEE Transactions on Cybernetics

DOI 10.1109/tcyb.2017.2671898

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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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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)
ORCID: Cheng, Jie-Zhi
Li, Shengli
Lei, Baiying
Wang, Tianfu
Ni, Dong
Venue: IEEE Transactions on Cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2017-05
Volume: 47
Issue: 5
Pages: 1336-1349
ISSN: 2168-2267 (ISSN-L)
2168-2275
DOI: 10.1109/tcyb.2017.2671898
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks

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