ArticleNeuroImage2022
Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Clinical Grading of Artificial Intelligence-Based 3D Fetal Brain Segmentations: A Cross-Vendor Evaluation of Deep Learning in Fetal Neuroimaging.Prenatal diagnosis · 2026Article
- SEG-FAUSP: Anatomical Structure Segmentation of the Standard Sections of Fetal Abdominal Ultrasounds.Bioengineering (Basel, Switzerland) · 2026Article
- A multi-task deep learning and radiomics framework for fetal anatomical structure detection and classification in ultrasound imaging.Scientific reports · 2026Article
- Deep learning assessment of fetal brain maturation on 3D ultrasound volumes in early-onset fetal growth restriction.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Normative growth trajectories of fetal brain regions validated by satisfactory maturation of neurodevelopmental domains at 2 years of age.Nature communications · 2026Article
- Cross-Modality Comparison of Fetal Brain Phenotypes: Insights From Short-Interval Second-Trimester MRI and Ultrasound Imaging.Human brain mapping · 2025Article
- Analysis of intra- and inter-observer variability in 4D liver ultrasound landmark labeling.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.Journal of clinical ultrasound : JCU · 2025Review
- Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.Chinese medical journal · 2025Review
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- Application of artificial intelligence in diagnosis and management of fetal growth disorders: a comprehensive review.Frontiers in medicine · 2025Review
- Automated Segmentation of Fetal Intracranial Volume in Three-Dimensional Ultrasound Using Deep Learning: Identifying Sex Differences in Prenatal Brain Development.Human brain mapping · 2024Article
- Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review.Journal of clinical medicine · 2024Review
- Neurodevelopmental disorders: 2024 update.Free neuropathology · 2024Article
- Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.Biomimetics (Basel, Switzerland) · 2023Review
- Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years.Nature · 2023Article
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation.Medical image analysis · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.