Evidence map›Paper›PMID 39247913›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2023

D2ANet: Densely Attentional-Aware Network for First Trimester Ultrasound CRL and NT Segmentation.

Mourad Gridach, Robail Yasrab, Lior Drukker, Aris T Papageorghiou, J Alison Noble

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Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Mourad GridachInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Robail YasrabInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Lior DrukkerNuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
Aris T PapageorghiouNuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
J Alison NobleInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.

Funding

European Research Council 694581
6 · The paper itself

Abstract

Manual annotation of medical images is time consuming for clinical experts; therefore, reliable automatic segmentation would be the ideal way to handle large medical datasets. In this paper, we are interested in detection and segmentation of two fundamental measurements in the first trimester ultrasound (US) scan: Nuchal Translucency (NT) and Crown Rump Length (CRL). There can be a significant variation in the shape, location or size of the anatomical structures in the fetal US scans. We propose a new approach, namely Densely Attentional-Aware Network for First Trimester Ultrasound CRL and NT Segmentation (DA2Net), to encode variation in feature size by relying on the powerful attention mechanism and densely connected networks. Our results show that the proposed D2ANet offers high pixel agreement (mean JSC = 84.21) with expert manual annotations.

Indexed as

Channel AttentionFirst TrimesterSpatial AttentionUltrasoundVideo Segmentation

Identifiers

PMID39247913
PMCPMC7616422

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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.