A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes

Dong, Jinbao; Liu, Shengfeng; Liao, Yimei; Wen, Huaxuan; Lei, Baiying; Li, Shengli; Wang, Tianfu

doi:10.1109/jbhi.2019.2948316

Cited by 104Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and EducationAdvanced Neural Network Applications

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A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes

Dong, Jinbao, Liu, Shengfeng, Liao, Yimei, Wen, Huaxuan, Lei, Baiying, Li, Shengli, Wang, Tianfu·2020·IEEE Journal of Biomedical and Health Informatics

DOI 10.1109/jbhi.2019.2948316

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Abstract

Quality control/assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP) is one of the most commonly used cardiac views, which was used in the diagnosis of heart anomalies in the early 1980s. In this paper, we propose a generic deep learning framework for automatic quality control of fetal US CFPs. The proposed framework consists of three networks: (1) a basic CNN (B-CNN), roughly classifying four-chamber views from the raw data; (2) a deeper CNN (D-CNN), determining the gain and zoom of the target images in a multi-task learning manner; and (3) the aggregated residual visual block net (ARVBNet), detecting the key anatomical structures on a plane. Based on the output of the three networks, overall quantitative score of each CFP is obtained, so as to achieve fully automatic quality control. Experiments on a fetal US dataset demonstrated our proposed method achieved a highest mean average precision (mAP) of 93.52% at a fast speed of 101 frames per second (FPS). In order to demonstrate the adaptability and generalization capacity, the proposed detection network (i.e., ARVBNet) has also been validated on the PASCAL VOC dataset, obtaining a highest mAP of 81.2% when input size is approximately 300 × 300.

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Authors: Dong, J., Liu, S., Liao, Y., Wen, H., Lei, B., Li, S., & Wang, T.
Affiliations: Shenzhen University (CN)
Shenzhen Maternity and Child Healthcare Hospital (CN)
ORCID: Dong, Jinbao
Liu, Shengfeng
Liao, Yimei
Wen, Huaxuan
Lei, Baiying
Li, Shengli
Wang, Tianfu
Venue: IEEE Journal of Biomedical and Health Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-04
Volume: 24
Issue: 4
Pages: 931-942
ISSN: 2168-2194 (ISSN-L)
2168-2208
DOI: 10.1109/jbhi.2019.2948316
OpenAlex: W2980762210
Cited by: 104
References: 55
Citations by year: 2026: 17 · 2025: 18 · 2024: 19 · 2023: 19 · 2022: 14 · 2021: 15 · 2020: 2
FWCI: 3.47
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Journal of Biomedical and Health Informatics (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (81571758, 81771922, 61871274, 61801305, 61501305)
National Key Research and Development Program of China Stem Cell and Translational Research (2016YFC0104700)
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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: prenatal ultrasound, image quality control, automated quality assessment, deep learning, multi-task learning, convolutional neural networks, object detection
MeSH terms: Female, Fetal Heart, Humans, Image Processing, Computer-Assisted, Pregnancy, Quality Control, Ultrasonography, Prenatal, Neural Networks, Computer

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A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes

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