Evidence map›Paper›PMID 36643819›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2022

End-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation.

Robail Yasrab, Zeyu Fu, Lior Drukker, Lok Hin Lee, He Zhao, Aris T Papageorghiou, Alison J Noble

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. D2ANet: Densely Attentional-Aware Network for First Trimester Ultrasound CRL and NT Segmentation.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023
    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

7 authors.

Robail YasrabInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Zeyu FuInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Lior DrukkerNuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
Lok Hin LeeInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
He ZhaoInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Aris T PapageorghiouNuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
Alison J NobleInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.

Funding

European Research Council 694581
6 · The paper itself

Abstract

This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.

Indexed as

crown rump length (CRL)First trimesternuchal translucency (NT)ultrasoundvideo segmentation

Identifiers

PMID36643819
PMCPMC7614066

What OpenQuestion holds

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Registered trials

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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.