Evidence map›Paper›PMID 38503854›Full record

ArticleScientific reports2024

Data-driven simulations for training AI-based segmentation of neutron images.

Pushkar S Sathe, Caitlyn M Wolf, Youngju Kim, Sarah M Robinson, M Cyrus Daugherty, Ryan P Murphy, Jacob M LaManna, Michael G Huber, David L Jacobson, Paul A Kienzle and 4 more

Abstract read
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In one paragraph

Article in Scientific reports, 2024. 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
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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

14 authors.

Pushkar S Sathe *Information Technology Laboratory, NIST, Gaithersburg, MD, 20899, USA.
Caitlyn M WolfNIST Center for Neutron Research, Gaithersburg, MD, 20899, USA.
Youngju KimPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
Sarah M RobinsonPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
M Cyrus DaughertyPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
Ryan P MurphyNIST Center for Neutron Research, Gaithersburg, MD, 20899, USA.
Jacob M LaMannaPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
Michael G HuberPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
David L JacobsonPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
Paul A KienzleNIST Center for Neutron Research, Gaithersburg, MD, 20899, USA.
Katie M WeigandtNIST Center for Neutron Research, Gaithersburg, MD, 20899, USA.
Nikolai N KlimovPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
Daniel S HusseyPhysical Measurement Laboratory, Gaithersburg, MD, 20899, USA.
Peter Bajcsy *Information Technology Laboratory, NIST, Gaithersburg, MD, 20899, USA. peter.bajcsy@nist.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs

Indexed as

Data-driven simulationINFERNeutron imagingSemantic segmentation

Identifiers

PMID38503854
PMCPMC10951284

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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.