ArticleAJNR. American journal of neuroradiology2023
Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.
Article in AJNR. American journal of neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Fetal brain MRI atlases and datasets: A review.NeuroImage · 2024Pooled it
- MRI-based spatio-temporal atlas of ganglionic eminence.European radiology experimental · 2026Article
- Extending the Branches of Neonatal Neurocritical Care.Seminars in perinatology · 2025Review
- Optimizing Performance of Transformer-based Models for Fetal Brain MR Image Segmentation.Radiology. Artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
background and purposeFetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages. MATERIALS AND
methodsThis retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation. A 3D U-Net was trained to segment the intracranial contents of 2D fetal brain MRIs in the axial, coronal, and sagittal planes. An additional 3D U-Net was trained to segment the brain from the output of the first model. Models were tested on MRIs of 10 patients (28 planes) via Dice coefficients and volume comparison with manual reference segmentations. Trained U-Nets were applied to 200 additional MRIs to develop normative reference intracranial and brain volumes across gestational ages and then to 9 pathologic fetal brains.
resultsFetal intracranial and brain compartments were automatically segmented in a mean of 6.8 (SD, 1.2) seconds with median Dices score of 0.95 and 0.90, respectively (interquartile ranges, 0.91-0.96/0.89-0.91) on the test set. Correlation with manual volume measurements was high (Pearson
conclusionsDeep learning techniques can quickly and accurately quantify intracranial and brain volumes on clinical fetal brain MRIs and identify abnormal volumes on the basis of a normative reference standard.
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Registered trials
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