Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning
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Abstract
The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.
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A limitation of AI US imaging in fetal neurosonography was the missing training of AI algorithms with images of pathologies.
“Other study limitations were the missing training of AI algorithms with images of pathologies [79,86,95]”
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SEG-FAUSP: Anatomical Structure Segmentation of the Standard Sections of Fetal Abdominal Ultrasounds
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Ultrasonography Uterus and Fetus Segmentation with Constrained Spatial-Temporal Memory FCN
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- Authors
- Hesse, L. S., Aliasi, M., Moser, F., Haak, M. C., Xie, W., Jenkinson, M., & Namburete, A. I. L.
- Affiliations
- University of Oxford (GB)
- Leiden University Medical Center (NL)
- Corresponding author
- Hesse, Linde S.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 254
- Pages
- 119117
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W4220855438
- Cited by
- 44
- References
- 65
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 6 · 2023: 6 · 2022: 8
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Royal Academy of Engineering
- Academy of Medical Sciences (SBF005\1136)
- Wellcome Trust (203139/Z/16/Z, 215573/Z/19/Z)
- University College, Oxford (EP/M013774/1, EP/T028572/1)
- Medical Research Council (EP/L016052/1)
- National Institute for Health Research
- Engineering and Physical Sciences Research Council
- 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 brain segmentation, subcortical structures, choroid plexus, cerebellum, 3D fetal ultrasound, convolutional neural networks, few-shot learning, weakly labeled data, atlas-based segmentation, growth curves, second trimester, prenatal neurosonography
- MeSH terms
- Deep Learning, Brain, Female, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography, Observer Variation, Neural Networks, Computer
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