Towards automated extraction of 2D standard fetal head planes from 3D ultrasound acquisitions: A clinical evaluation and quality assessment comparison
Open accessAbstract
INTRODUCTION: Clinical evaluation of deep learning (DL) tools is essential to compliment technical accuracy metrics. This study assessed the image quality of standard fetal head planes automatically-extracted from three-dimensional (3D) ultrasound fetal head volumes using a customised DL-algorithm. METHODS: weeks). Each had two-dimensional (2D) manually-acquired (2D-MA), 3D operator-selected (3D-OS) and 3D-DL automatically-acquired (3D-DL) images. The proportion of adequate images from each plane and modality, and the number of inadequate images per plane was compared for each method. Inter and intra-observer agreement of overall image quality was calculated. RESULTS: Sixty-seven percent of 3D-OS and 3D-DL transventricular planes were adequate quality. Forty-five percent of 3D-OS and 55% of 3D-DL transcerebellar planes were adequate. Seventy-one percent of 3D-OS and 86% of 3D-DL transventricular planes failed with poor visualisation of intra-cranial structures. Eighty-six percent of 3D-OS and 80% of 3D-DL transcerebellar planes failed due to inadequate visualisation of cerebellar hemispheres. Image quality was significantly different between 2D and 3D, however, no significant difference between 3D-modalities was demonstrated (p < 0.005). Inter-observer agreement of transventricular plane adequacy was moderate for both 3D-modalities, and weak for transcerebellar planes. CONCLUSION: The 3D-DL algorithm can automatically extract standard fetal head planes from 3D-head volumes of comparable quality to operator-selected planes. Image quality in 3D is inferior to corresponding 2D planes, likely due to limitations with 3D-technology and acquisition technique. IMPLICATIONS FOR PRACTICE: Automated image extraction of standard planes from US-volumes could facilitate use of 3DUS in clinical practice, however image quality is dependent on the volume acquisition technique.
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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].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [92]
The Methods (Quality assessment; page unavailable) state that two human observers assessed whether each plane was adequate. The model automatically extracted planes from 3D images; it did not perform the image-quality assessment attributed to it. (LLM, conf=0.88)
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References 33 · Cited by 11
- Authors
- Skelton, E., Matthew, J., Li, Y., Khanal, B., Cerrolaza Martinez, J. J., Toussaint, N., Gupta, C., Knight, C., Kainz, B., Hajnal, J. V., & Rutherford, M.
- Affiliations
- King's College London (GB)
- Imperial College London (GB)
- Corresponding author
- Skelton, E.
- ORCID
- Venue
- Radiography
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 27
- Issue
- 2
- Pages
- 519-526
- ISSN
- 1078-8174 (ISSN-L)
- PMC
- PMC8052189
- OpenAlex
- W3107381730
- Cited by
- 11
- References
- 33
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 5
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Radiography (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Funders
- Wellcome Trust
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- 3D ultrasound, deep learning, image quality, observer agreement
- MeSH terms
- Female, Gestational Age, Head, Humans, Infant, Newborn, Pregnancy, Retrospective Studies, Ultrasonography, Prenatal, Imaging, Three-Dimensional
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