Cited by 164Medical Image Segmentation TechniquesPrenatal Screening and DiagnosticsArtificial Intelligence in Healthcare and Education

Ultrasound Standard Plane Detection Using a Composite Neural Network Framework

Chen, Hao, Wu, Lingyun, Dou, Qi, Qin, Jing, Li, Shengli, Cheng, Jie-Zhi, Ni, Dong, Heng, Pheng-Ann·2017·IEEE Transactions on Cybernetics

Abstract

Ultrasound (US) imaging is a widely used screening tool for obstetric examination and diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is very crucial for substantial biometric measurement and diagnosis. However, the standard plane acquisition is a labor-intensive task and requires operator equipped with a thorough knowledge of fetal anatomy. Therefore, automatic approaches are highly demanded in clinical practice to alleviate the workload and boost the examination efficiency. The automatic detection of standard planes from US videos remains a challenging problem due to the high intraclass and low interclass variations of standard planes, and the relatively low image quality. Unlike previous studies which were specifically designed for individual anatomical standard planes, respectively, we present a general framework for the automatic identification of different standard planes from US videos. Distinct from conventional way that devises hand-crafted visual features for detection, our framework explores in- and between-plane feature learning with a novel composite framework of the convolutional and recurrent neural networks. To further address the issue of limited training data, a multitask learning framework is implemented to exploit common knowledge across detection tasks of distinctive standard planes for the augmentation of feature learning. Extensive experiments have been conducted on hundreds of US fetus videos to corroborate the better efficacy of the proposed framework on the difficult standard plane detection problem.

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Crossref · 10.1109/tcyb.2017.2685080

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Authors
Chen, H., Wu, L., Dou, Q., Qin, J., Li, S., Cheng, J.-Z., Ni, D., & Heng, P.-A.
Affiliations
  • Chinese University of Hong Kong (HK)
  • Shenzhen University (CN)
  • Hong Kong Polytechnic University (HK)
  • Shenzhen Maternity and Child Healthcare Hospital (CN)
Venue
IEEE Transactions on Cybernetics
Published
2017-06
Volume
47
Issue
6
Pages
1576-1586
ISSN
  • 2168-2267 (ISSN-L)
  • 2168-2275
OpenAlex
W2598799153
Cited by
164
References
65
Citations by year
2026: 11 · 2025: 12 · 2024: 26 · 2023: 24 · 2022: 25 · 2021: 19 · 2020: 19 · 2019: 16 · 2018: 6 · 2017: 6
FWCI
5.34
Type
journal article
Language
en
Open access
closed
Locations
  • IEEE Transactions on Cybernetics (publishedVersion)
  • PubMed (publishedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Funders
  • National Basic Research Program of China, 973 Program (2015CB351706)
  • National Natural Science Foundation of China (61571304, 61501305, 61233012)
  • Research Grants Council of Hong Kong Special Administrative Region (CUHK 14202514)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
fetal anatomy, prenatal ultrasound, convolutional recurrent neural network, multi-task learning, representation learning, limited training data, fetal biometry
MeSH terms
Algorithms, Female, Fetus, Humans, Image Processing, Computer-Assisted, Pregnancy, Video Recording, Ultrasonography, Prenatal, Neural Networks, Computer

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