Evidence map›Paper›PMID 41953176›Full record

ArticlePerinatal, preterm and paediatric image analysis (2025)2025

Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series.

Yingcheng Liu, Neerav Karani, Neel Dey, S Mazdak Abulnaga, Junshen Xu, P Ellen Grant, Esra Abaci Turk, Polina Golland

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Perinatal, preterm and paediatric image analysis (2025), 2025. 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. Fetuses Made Simple: Modeling and Tracking of Fetal Shape and Pose.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yingcheng LiuComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Neerav KaraniComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Neel DeyComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
S Mazdak AbulnagaComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Junshen XuDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
P Ellen GrantFetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Esra Abaci TurkFetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Polina GollandComputer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.

Funding

Training and DisseminationP41EB015902 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI KIKINIS, RON, WESTIN, CARL-FREDRIK · 2012 to 2022
$20.9M
Novel MRI Assessment of Placental Structure and Function Throughout PregnancyR01HD100009 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI ADALSTEINSSON, ELFAR, GRANT, PATRICIA ELLEN · 2019 to 2023
$3.6M
Fetal MRI: robust self-driving brain acquisition and body movement quantificationR01EB032708 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI ADALSTEINSSON, ELFAR, GRANT, PATRICIA ELLEN · 2022 to 2025
$2.8M
Characterizing the Relationship between Uterine Activity and Placental Function Across Pregnancy with MRIR21HD106553 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI ABACI TURK, ESRA · 2022 to 2023
$497k
NIBIB NIH HHS P41 EB015902NIBIB NIH HHS R01 EB032708NICHD NIH HHS R01 HD100009NICHD NIH HHS R21 HD106553
6 · The paper itself

Abstract

The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effective semi-supervised learning method for improving placenta segmentation in fetal EPI MRI time series. We employ consistency regularization loss that promotes consistency under spatial transformation of the same image and temporal consistency across nearby images in a time series. The experimental results show that the method improves the overall segmentation accuracy and provides better performance for outliers and hard samples. The evaluation also indicates that our method improves the temporal coherency of the prediction, which could lead to more accurate computation of temporal placental biomarkers. This work contributes to the study of the placenta and prenatal clinical decision-making. Code is available at https://github.com/firstmover/cr-seg.

Indexed as

consistency regularizationFetal MRIimage segmentationplacenta segmentationsemi-supervised learning (SSL)

Identifiers

PMID41953176
PMCPMC13055010

What OpenQuestion holds

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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.