ArticleEuropean radiology2024
Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Age-Specific Contrast Optimization of bSSFP in Fetal Brain.Magnetic resonance in medicine · 2026Article
- In Vivo Assessment of Placental Structure and Perfusion in Late-Gestation Pregnancies and Their Association With Fetal Growth.NMR in biomedicine · 2026Article
- Normative volumetric growth modeling of the whole fetal body, placenta, and amniotic fluid for three-dimensional T2-weighted magnetic resonance imaging.Pediatric radiology · 2026Article
- Time-dependent Diffusion MRI for Predicting Response to Induction Chemotherapy in Nasopharyngeal Carcinoma.Radiology. Imaging cancer · 2026Article
- ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume.Journal of imaging informatics in medicine · 2026Article
- Multi-scanner repeatability and reproducibility of qRF-MRF breath-held and free-breathing with pilot tone in the abdomen.Magma (New York, N.Y.) · 2026Article
- Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta, and umbilical cord for fetal MRI at 0.55T.Magnetic resonance in medicine · 2026Article
- Normative volumetric growth modeling of the whole fetal body, placenta and amniotic fluid for 3-dimensional T2-weighted magnetic resonance imaging.medRxiv : the preprint server for health sciences · 2026Article
- Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI.Frontiers in neuroscience · 2026Article
- MRI-based Quantification of Ossification Centers in the Human Fetal Atlas and Axis Vertebrae.Current medical imaging · 2026Article
- Fetal Assessment Suite (FetAS): a web-based platform for automatic fetal MRI analysis using AI.Scientific reports · 2025Article
- Predicting adverse perinatal outcomes in small-for-gestational-age fetuses using MRI, ultrasound, and clinical data.Radiology advances · 2025Article
- Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers.Scientific reports · 2025Article
- Fetal MRI deep learning segmentation of body and lung in congenital diaphragmatic hernia.Radiology advances · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesTo develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR).
methodsRetrospective data of 348 fetuses with gestational age (GA) of 19-39 weeks were included: 249 normal appropriate for GA (AGA), 19 FGR, and 80 Other (having various imaging abnormalities). A fetal whole-body segmentation model with a quality estimation module was developed and evaluated in 169 cases. The method was evaluated for its repeatability (repeated scans within the same scanner, n = 22), reproducibility (different scanners, n = 6), and accuracy (compared with birth weight, n = 7). A normal MRI-based growth chart was derived.
resultsThe method achieved a Dice = 0.973, absolute volume difference ratio (VDR) = 1.8% and VDR mean difference = 0.75% ([Formula: see text]: - 3.95%, 5.46), and high agreement with the gold standard. The method achieved a repeatability coefficient = 4.01%, ICC = 0.99, high reproducibility with a mean difference = 2.21% ([Formula: see text]: - 1.92%, 6.35%), and high accuracy with a mean difference between estimated fetal weight (EFW) and birth weight of - 0.39% ([Formula: see text]: - 8.23%, 7.45%). A normal growth chart (n = 246) was consistent with four ultrasound charts. EFW based on MRI correctly predicted birth-weight percentiles for all 18 fetuses ≤ 10
conclusionsThe proposed method for automatic MRI-based EFW demonstrated high performance and sensitivity to identify FGR fetuses. CLINICAL RELEVANCE STATEMENT: Results from this study support the use of the automatic fetal weight estimation method based on MRI for the assessment of fetal development and to detect fetuses at risk for growth restriction. KEY POINTS: • An AI-based segmentation method with a quality assessment module for fetal weight estimation based on MRI was developed, achieving high repeatability, reproducibility, and accuracy. • An MRI-based fetal weight growth chart constructed from a large cohort of normal and appropriate gestational-age fetuses is proposed. • The method showed a high sensitivity for the diagnosis of small fetuses suspected of growth restriction.
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