A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints
Abstract
During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images of the fetal brain have been correctly acquired and meet the required clinical standard. We propose a deep learning solution, which breaks the problem down into three stages: (i) accurate localization of the fetal brain, (ii) detection of regions that contain structures of interest and (iii) learning the acoustic patterns in the regions that enable plane verification. We evaluate the developed methodology on a large real-world clinical data set of 2-D mid-gestation fetal images. We show that the automatic verification method approaches human expert assessment.
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Models by Lin et al., Yaqub et al., and Skelton et al. effectively performed image quality assessment of whether a standard plane was correctly acquired.
“The image quality assessment of whether a standard plane was correctly acquired, either by human operators or by automated extraction from 3D images, was effectively performed by models developed by the research groups of Lin et al. [90], Yaqub et al. [91], and Skelton et al. [92].”
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- Authors
- Yaqub, M., Kelly, B., Papageorghiou, A. T., & Noble, J. A.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Yaqub, Mohammad
- Venue
- Ultrasound in Medicine & Biology
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 43
- Issue
- 12
- Pages
- 2925-2933
- ISSN
- 0301-5629 (ISSN-L)
- OpenAlex
- W2759207985
- Cited by
- 67
- References
- 28
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 7 · 2023: 9 · 2022: 9 · 2021: 5 · 2020: 12 · 2019: 8 · 2018: 4
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ultrasound in Medicine & Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- UK Engineering and Physical Sciences Research Council (EPSRC) (EP/L505316/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- fetal neurosonography, fetal brain biometry, fetal brain anomalies, deep learning, brain localization
- MeSH terms
- Machine Learning, Brain, Female, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography, Prenatal, Neural Networks, Computer
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