Evidence map›Paper›PMID 41646770›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Normative volumetric growth modeling of the whole fetal body, placenta and amniotic fluid for 3-dimensional T2-weighted magnetic resonance imaging.

Alena Uus, Megan Hall, Charline Bradshaw, Anangsha Kumar, Jordina Aviles Verdera, Sara Neves Silva, Aysha Luis, Hadi Waheed, Pedro Alarcon Gil, Lucilio Cordero-Grande and 9 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Alena Uus*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Megan Hall*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Charline Bradshaw*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Anangsha Kumar*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Jordina Aviles Verdera*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Sara Neves Silva*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Aysha Luis*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Hadi Waheed*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Pedro Alarcon GilBiomedical Image Technologies, ETSI Telecomunicacion, Universidad Politćcnica de Madrid and CIBER-BBN, Madrid, Spain.
Lucilio Cordero-Grande*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Jacqueline Matthew*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Vanessa Kyriakopoulou*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Maria Deprez*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Kathleen Colford*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Alexia Egloff Collado*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Joseph V Hajnal*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Mary Rutherford*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Jana Hutter*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Lisa Story*Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Funding

Structure and function of the placenta from implantation to delivery: a next generation MRI approachU01HD087202 · NICHD · KING'S COLLEGE LONDON · PI ALEXANDER, DANIEL, ALJABAR, PAUL · 2015 to 2015
$3.2M
NICHD NIH HHS U01 HD087202Wellcome Trust
6 · The paper itself

Abstract

Background: Volumetric assessment of the fetus, placenta and amniotic fluid is clinically valuable, but MRI volumetry is rarely performed in clinical practice because of the required labour-intensive manual segmentation of motion-corrupted 2-dimensional (2-D) stacks. Existing deep-learning approaches typically segment single structures in 2-D motion-corrupted stacks, are, however limited in accuracy by slice misalignment. No current method provides a reliable automated solution for whole-uterus volumetry in 3-D reconstructed MRI. Furthermore, normative ranges for computation of centiles are currently missing. Objective: To develop an automated pipeline for whole-uterus volumetry in 3-D T2-weighted fetal MRI and to generate normative growth models for fetal, placental and amniotic fluid volumes in healthy pregnancies with confirmed delivery at term. Materials and methods: Deformable slice-to-volume 3-D reconstruction was applied to motion-corrupted T2-weighted (T2W) stacks from 0.55T-3T MRI, and a 3-D UNet was trained to segment fetus, placenta and amniotic fluid on the resulting reconstructed 3-D images. A reporting tool generates centiles, z-scores and structured HTML outputs. Automated segmentation was performed in 357 healthy control datasets from 16-41 weeks gestational age (GA) range with confirmed delivery at term. After visual checks of segmeted labels and minor refinements, GA-based quadratic normative volumetry models were derived and correlations with maternal and fetal characteristics assessed. The utility of the pipeline for clinical research was further evaluated using 95 longitudinal scans from 42 fetuses and 86 preterm (≤ 32 weeks at delivery) pregnancies. Results: Automated segmentation produced accurate 3-D labels, with only small local corrections (< Conclusion: This study introduces an automated whole-uterus volumetry pipeline and corresponding normative 3-D MRI growth models. The method provides robust, standardised volumetric assessment of fetal, placental and amniotic fluid development and offers a practical tool for evaluating growth patterns in both normal and high-risk pregnancies.

Indexed as

amniotic fluidfetusmagnetic resonance imagingnormative modelingplacentavolumetry

Identifiers

PMID41646770
PMCPMC12870648

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.