Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI | Paper Citation Record

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This paper cites Fetal biometry to assess the size and growth of the fetus.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Fetal biometry to assess the size and growth of the fetus

Reference 1

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This paper cites Automatic biometry of fetal brain MRIs using deep and machine learning techniques.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Automatic biometry of fetal brain MRIs using deep and machine learning techniques

Reference 2

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This paper cites Towards automated fetal brain biometry reporting for 3-dimensional T2-weighted 0.55–3T magnetic resonance imaging at 20–40 weeks gestational age range.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Towards automated fetal brain biometry reporting for 3-dimensional T2-weighted 0.55–3T magnetic resonance imaging at 20–40 weeks gestational age range

Reference 3

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This paper cites Automatic linear measurements of the fetal brain on MRI with deep neural networks.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Automatic linear measurements of the fetal brain on MRI with deep neural networks

Reference 4

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This paper cites Corpus callosum length by gestational age as evaluated by fetal MR imaging.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Corpus callosum length by gestational age as evaluated by fetal MR imaging

Reference 5

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This paper cites Artificial intelligence in fetal brain imaging: advancements, challenges, and multimodal approaches for biometric and structural analysis.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Artificial intelligence in fetal brain imaging: advancements, challenges, and multimodal approaches for biometric and structural analysis

Reference 6

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This paper cites Normative linear and volumetric biometric measurements of fetal brain development in magnetic resonance imaging.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Normative linear and volumetric biometric measurements of fetal brain development in magnetic resonance imaging

Reference 7

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This paper cites Fetal central nervous system biometry on MR imaging.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Fetal central nervous system biometry on MR imaging

Reference 8

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This paper cites Motion - compensation techniques in neonatal and fetal MR imaging.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Motion - compensation techniques in neonatal and fetal MR imaging

Reference 9

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This paper cites Fetal MRI: what’s new? A short review.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Fetal MRI: what’s new? A short review

Reference 10

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This paper cites Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Reference 11

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This paper cites Enhancing prenatal diagnosis: automated fetal brain MRI morphometry.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Enhancing prenatal diagnosis: automated fetal brain MRI morphometry

Reference 12

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This paper cites Geometric reliability of super- resolution reconstructed images from clinical fetal MRI in the second trimester.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Geometric reliability of super- resolution reconstructed images from clinical fetal MRI in the second trimester

Reference 13

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This paper cites Deep learning model for predicting gestational age after the first trimester using fetal MRI.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Deep learning model for predicting gestational age after the first trimester using fetal MRI

Reference 14

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This paper cites Automatic ventriculomegaly detection in fetal brain MRI: a step-by-step deep learning model for novel 2D–3D linear measurements.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Automatic ventriculomegaly detection in fetal brain MRI: a step-by-step deep learning model for novel 2D–3D linear measurements

Reference 15

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This paper cites Fetal gestational age prediction in brain MRI using artificial intelligence: a comparative study of three biometric techniques.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Fetal gestational age prediction in brain MRI using artificial intelligence: a comparative study of three biometric techniques

Reference 16

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This paper cites Fetal brain MRI measurements using a deep learning landmark network with reliability estimation, in: Sudre, C.H., et al.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Fetal brain MRI measurements using a deep learning landmark network with reliability estimation, in: Sudre, C.H., et al

Reference 17

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This paper cites MICCAI 2024 Challenge, 2024.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI MICCAI 2024 Challenge, 2024

Reference 18

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This paper cites Advances in automated fetal brain MRI segmentation and biometry: insights from the FeTA 2024 challenge.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Advances in automated fetal brain MRI segmentation and biometry: insights from the FeTA 2024 challenge

Reference 19

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This paper cites https://www.developingconnectome.org (accessed 6 February 2026).

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI https://www.developingconnectome.org (accessed 6 February 2026)

Reference 20

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This paper cites An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

Reference 21

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This paper cites The Medical Image Analysis Laboratory Super -Resolution ToolKit (MIALSRTK) v2.0.0 (Version 2.0.0) [software].

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI The Medical Image Analysis Laboratory Super -Resolution ToolKit (MIALSRTK) v2.0.0 (Version 2.0.0) [software]

Reference 22

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This paper cites Image Registration Toolkit (IRTK) (Version accessed 2025) [software].

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Image Registration Toolkit (IRTK) (Version accessed 2025) [software]

Reference 23

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This paper cites https://www.synapse.org (accessed 6 February 2026).

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI https://www.synapse.org (accessed 6 February 2026)

Reference 24

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This paper cites The developing human connectome project (dHCP): fetal acquisition protocol.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI The developing human connectome project (dHCP): fetal acquisition protocol

Reference 25

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This paper cites Automatic whole brain MRI segmentation of the developing neonatal brain.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Automatic whole brain MRI segmentation of the developing neonatal brain

Reference 26

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This paper cites Region Annotation With Expectation-Maximization (Draw-EM) (Version accessed 2025) [software].

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Region Annotation With Expectation-Maximization (Draw-EM) (Version accessed 2025) [software]

Reference 27

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This paper cites BOUNTI: brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI BOUNTI: brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI

Reference 28

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This paper cites U., Kyriakopoulou, V., Makropoulos, A., Fukami-Gartner, A., Cromb, D., Davidson, A., et al., 2023.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI U., Kyriakopoulou, V., Makropoulos, A., Fukami-Gartner, A., Cromb, D., Davidson, A., et al., 2023

Reference 29

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This paper cites Fetal brain biometric measurements on 3D super-resolution reconstructed T2-weighted MRI: an intra- and inter-observer agreement study.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Fetal brain biometric measurements on 3D super-resolution reconstructed T2-weighted MRI: an intra- and inter-observer agreement study

Reference 30

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Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Unresolved cited work

Reference 31

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This paper cites Multi -channel spatio-temporal MRI atlas of the normal fetal brain development from the developing Human Connectome Project.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Multi -channel spatio-temporal MRI atlas of the normal fetal brain development from the developing Human Connectome Project

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Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Advanced normalization tools (ANTS)

Reference 33

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Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Advanced Normalization Tools (ANTs) v2.5.3 (Version 2.5.3)[software]

Reference 34

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This paper cites Deep learning-based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Deep learning-based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy

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This paper cites A guideline of selecting and reporting intraclass correlation coefficients for reliability research.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI A guideline of selecting and reporting intraclass correlation coefficients for reliability research

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This paper cites auto -proc-svrtk (Version accessed 2025) [software].

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI auto -proc-svrtk (Version accessed 2025) [software]

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This paper cites Using Cliff’s delta as a non-parametric effect size measure: an accessible Web App and R tutorial.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Using Cliff’s delta as a non-parametric effect size measure: an accessible Web App and R tutorial

Reference 38

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This paper cites ISUOG practice guidelines: ultrasound assessment of fetal biometry and growth.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI ISUOG practice guidelines: ultrasound assessment of fetal biometry and growth

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This paper cites Fetal brain MRI: neurometrics, typical diagnoses, and resolving common dilemmas.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Fetal brain MRI: neurometrics, typical diagnoses, and resolving common dilemmas

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This paper cites ISUOG practice guidelines (updated): performance of fetal magnetic resonance imaging.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI ISUOG practice guidelines (updated): performance of fetal magnetic resonance imaging

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This paper cites The reliability of fetal MRI in the assessment of brain malformations.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI The reliability of fetal MRI in the assessment of brain malformations

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This paper cites Prenatal magnetic resonance imaging: brain normal linear biometric values below 24 gestational weeks.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Prenatal magnetic resonance imaging: brain normal linear biometric values below 24 gestational weeks

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This paper cites Detecting anatomical landmarks from limited medical imaging data using two -stage task-oriented deep neural networks.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Detecting anatomical landmarks from limited medical imaging data using two -stage task-oriented deep neural networks

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This paper cites Uncertainty estimation in landmark localization based on Gaussian heatmaps.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Uncertainty estimation in landmark localization based on Gaussian heatmaps

Reference 45

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This paper cites BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes

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This paper cites Cerebral biometry in fetal magnetic resonance imaging: new reference data.

Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Cerebral biometry in fetal magnetic resonance imaging: new reference data

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correction dated 2011-01-24. Source: crossref record 10.1002/uog.8939->10.1002/uog.6276:correction, observed 2026-07-11T03:12:47.386825+00:00. This notice travels one citation hop only.

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Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI Unresolved cited work

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