Evidence map›Paper›PMID 30030766›Full record

ArticleJournal of digital imaging2019

Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts.

Dominique Duncan, Rachael Garner, Ivan Zrantchev, Tyler Ard, Bradley Newman, Adam Saslow, Emily Wanserski, Arthur W Toga

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of digital imaging, 2019. 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. Review
  2. 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.

Dominique DuncanLaboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA, 90033, USA. Dominique.Duncan@loni.usc.edu.ORCID 0000-0002-6154-9262
Rachael GarnerLaboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA, 90033, USA.
Ivan ZrantchevLaboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA, 90033, USA.
Tyler ArdLaboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA, 90033, USA.
Bradley NewmanRareFaction Interactive, 1725 Camino Palmero Street, 410, Los Angeles, CA, 90046, USA.
Adam SaslowRareFaction Interactive, 1725 Camino Palmero Street, 410, Los Angeles, CA, 90046, USA.
Emily WanserskiRareFaction Interactive, 1725 Camino Palmero Street, 410, Los Angeles, CA, 90046, USA.
Arthur W TogaLaboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave., Los Angeles, CA, 90033, USA.

Funding

TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
NIBIB NIH HHS P41 EB015922NIH HHS U54EB0020406
6 · The paper itself

Abstract

Segmentation of MRI scans is a critical part of the workflow process before we can further analyze neuroimaging data. Although there are several automatic tools for segmentation, no segmentation software is perfectly accurate, and manual correction by visually inspecting the segmentation errors is required. The process of correcting these errors is tedious and time-consuming, so we present a novel method of performing this task in a head-mounted virtual reality interactive system with a new software, Virtual Brain Segmenter (VBS). We provide the results of user testing on 30 volunteers to show the benefits of our tool as a more efficient, intuitive, and engaging alternative compared with the current method of correcting segmentation errors.

Indexed as

Virtual RealityBrainBrain MappingClinical CompetenceDiagnostic ErrorsHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingNeuroimagingImagingMRIQuality controlSegmentationVirtual reality

Identifiers

PMID30030766
PMCPMC6382628

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.