Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search
Abstract
Acquisition of the standard plane is crucial for medical ultrasound diagnosis. However, this process requires substantial experience and a thorough knowledge of human anatomy. Therefore it is very challenging for novices and even time consuming for experienced examiners. We proposed a hierarchical, supervised learning framework for automatically detecting the standard plane from consecutive 2-D ultrasound images. We tested this technique by developing a system that localizes the fetal abdominal standard plane from ultrasound video by detecting three key anatomical structures: the stomach bubble, umbilical vein and spine. We first proposed a novel radial component-based model to describe the geometric constraints of these key anatomical structures. We then introduced a novel selective search method which exploits the vessel probability algorithm to produce probable locations for the spine and umbilical vein. Next, using component classifiers trained by random forests, we detected the key anatomical structures at their probable locations within the regions constrained by the radial component-based model. Finally, a second-level classifier combined the results from the component detection to identify an ultrasound image as either a "fetal abdominal standard plane" or a "non- fetal abdominal standard plane." Experimental results on 223 fetal abdomen videos showed that the detection accuracy of our method was as high as 85.6% and significantly outperformed both the full abdomen and the separate anatomy detection methods without geometric constraints. The experimental results demonstrated that our system shows great promise for application to clinical practice.
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Some authors noted the missing possibility for real-time application of various AI models, especially those designed for intrapartum application.
“some authors noted the missing possibility for real-time application of various AI models, interestingly affecting especially those that are designed for intrapartum application [42,127,172,173,174].”
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- Authors
- Ni, D., Yang, X., Chen, X., Chin, C.-T., Chen, S., Heng, P. A., Li, S., Qin, J., & Wang, T.
- Affiliations
- Shenzhen University (CN)
- Chinese University of Hong Kong (HK)
- Shenzhen Maternity and Child Healthcare Hospital (CN)
- Corresponding author
- Li, Shengli
- Qin, Jing
- Wang, Tianfu
- Venue
- Ultrasound in Medicine & Biology
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 40
- Issue
- 11
- Pages
- 2728-2742
- ISSN
- 0301-5629 (ISSN-L)
- OpenAlex
- W2034842090
- Cited by
- 65
- References
- 30
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 9 · 2022: 7 · 2021: 8 · 2020: 5 · 2019: 5 · 2018: 5 · 2017: 9 · 2016: 4 · 2015: 6
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ultrasound in Medicine & Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61101026, 81270707, 61271108, 61233012)
- Natural Science Foundation of Guangdong (S2013010014973)
- Council of Hong Kong (CUHK412510)
- 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 › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- anatomical structure detection, selective search, umbilical vein, random forest classification, geometric constraints, ultrasound video analysis
- MeSH terms
- Abdomen, Algorithms, Female, Fetus, Humans, Pattern Recognition, Automated, Pregnancy, Spine, Stomach, Umbilical Veins, Reproducibility of Results, Ultrasonography, Prenatal, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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