Evidence map›Paper›PMID 36303792›Full record

ArticleFrontiers in bioinformatics2021

New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing.

Corentin Guérinot, Valentin Marcon, Charlotte Godard, Thomas Blanc, Hippolyte Verdier, Guillaume Planchon, Francesca Raimondi, Nathalie Boddaert, Mariana Alonso, Kurt Sailor and 4 more

Abstract read
In one paragraph

Article in Frontiers in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Metaverse Wearables for Immersive Digital Healthcare: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Review
  4. 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

14 authors.

Corentin GuérinotDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.
Valentin MarconDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.
Charlotte GodardDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.
Thomas BlancSorbonne Université, Collège Doctoral, Paris, France.
Hippolyte VerdierDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.
Guillaume PlanchonDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.
Francesca RaimondiDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.
Nathalie BoddaertPediatric Radiology Unit, Hôpital Universitaire Necker-Enfants Malades, Université de Paris, Paris, France.
Mariana AlonsoPerception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France.
Kurt SailorPerception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France.
Pierre-Marie LledoPerception and Memory Unit, CNRS UMR3571, Institut Pasteur, Paris, France.
Bassam HajjSorbonne Université, Collège Doctoral, Paris, France.
Mohamed El BeheiryDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.
Jean-Baptiste MassonDecision and Bayesian Computation, USR 3756 (C3BI/DBC) & Neuroscience Department CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Three-dimensional imaging is at the core of medical imaging and is becoming a standard in biological research. As a result, there is an increasing need to visualize, analyze and interact with data in a natural three-dimensional context. By combining stereoscopy and motion tracking, commercial virtual reality (VR) headsets provide a solution to this critical visualization challenge by allowing users to view volumetric image stacks in a highly intuitive fashion. While optimizing the visualization and interaction process in VR remains an active topic, one of the most pressing issue is how to utilize VR for annotation and analysis of data. Annotating data is often a required step for training machine learning algorithms. For example, enhancing the ability to annotate complex three-dimensional data in biological research as newly acquired data may come in limited quantities. Similarly, medical data annotation is often time-consuming and requires expert knowledge to identify structures of interest correctly. Moreover, simultaneous data analysis and visualization in VR is computationally demanding. Here, we introduce a new procedure to visualize, interact, annotate and analyze data by combining VR with cloud computing. VR is leveraged to provide natural interactions with volumetric representations of experimental imaging data. In parallel, cloud computing performs costly computations to accelerate the data annotation with minimal input required from the user. We demonstrate multiple proof-of-concept applications of our approach on volumetric fluorescent microscopy images of mouse neurons and tumor or organ annotations in medical images.

Indexed as

cloud computationCT-scanhuman-in-the-loopinferenceMRIone-shot learningvirtual reality

Identifiers

PMID36303792
PMCPMC9580868

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.