Evidence map›Paper›PMID 39540004›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2022

NEC-NET : Segmentation and Feature Extraction Network for the Neurocranium in Early Childhood.

Di Fan, Niharika Gajawelli, Athelia Paulli, Eryn Perry, Jeff Tanedo, Sean Deoni, Yalin Wang, Marius George Linguraru, Natasha Lepore

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Di FanCIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Niharika GajawelliCIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Athelia PaulliCIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Eryn PerryCIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Jeff TanedoCIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Sean DeoniBill and Melinda Gates Foundation, Seattle, WA, USA.
Yalin WangSchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
Marius George LinguraruDepartments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, D.C, USA.
Natasha LeporeCIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.

Funding

Early joint cranial and brain development from fetal and pediatric imagingR01DE030286 · NIDCR · CHILDREN'S HOSPITAL OF LOS ANGELES · PI LEPORE, NATASHA, LINGURARU, MARIUS GEORGE · 2021 to 2025
$3.5M
Predicting the early childhood outcomes of preterm brain shape abnormalitiesR01EB025032 · NIBIB · CHILDREN'S HOSPITAL OF LOS ANGELES · PI LEPORE, NATASHA · 2017 to 2020
$2.6M
NIBIB NIH HHS R01 EB025032NIDCR NIH HHS R01 DE030286
6 · The paper itself

Abstract

In early life, the neurocranium undergoes rapid changes to accommodate the expanding brain. Neurocranial maturation can be disrupted by developmental abnormalities and environmental factors such as sleep position. To establish a baseline for the early detection of anomalies, it is important to understand how this structure typically grows in healthy children. Here, we designed a deep neural network pipeline NEC-NET, including segmentation and classification, to analyze the normative development of the neurocranium in T1 MR images from healthy children aged 12 to 60 months old. The pipeline optimizes the segmentation of the neurocranium and shows the preliminary results of age-based regional differences among infants.

Indexed as

brainclassificationNeurocraniumsegmentation

Identifiers

PMID39540004
PMCPMC11557371

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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.