Evidence map›Paper›PMID 30078151›Full record

ArticleInternational journal of computer assisted radiology and surgery2019

Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images.

Marc Aubreville, Maike Stoeve, Nicolai Oetter, Miguel Goncalves, Christian Knipfer, Helmut Neumann, Christopher Bohr, Florian Stelzle, Andreas Maier

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.6field-weighted citation impact, top 6% of its field
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

20 citing papers in PubMed, 48 citations in OpenAlex.

  1. Article
  2. Article
  3. Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
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  7. Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2022
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  12. Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2021
    Article
  13. Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021
    Review
  14. Review
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  18. SCREENING FOR BARRETT'S ESOPHAGUS WITH PROBE-BASED CONFOCAL LASER ENDOMICROSCOPY VIDEOS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020
    Article
  19. Deep transfer learning methods for colon cancer classification in confocal laser microscopy images.International journal of computer assisted radiology and surgery · 2019
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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 at 4 institutions in 1 country.

Marc AubrevillePattern Recognition Lab, Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. marc.aubreville@fau.de.ORCID http://orcid.org/0000-0002-5294-5247
Maike StoevePattern Recognition Lab, Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Nicolai OetterDepartment of Oral and Maxillofacial Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Miguel GoncalvesDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Christian KnipferDepartment of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Universität Hamburg, Hamburg, Germany.
Helmut NeumannFirst Department of Internal Medicine, University Hospital Mainz, Johannes Gutenberg-Universität Mainz, Mainz, Germany.
Christopher BohrDepartment of Otorhinolaryngology, Head and Neck Surgery, University Hospital, Universität Regensburg, Regensburg, Germany.
Florian StelzleDepartment of Oral and Maxillofacial Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Andreas MaierPattern Recognition Lab, Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Friedrich-Alexander-Universität Erlangen-Nürnberg · DEJohannes Gutenberg University Mainz · DEUniversität Hamburg · DEUniversity Hospital Regensburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeProbe-based confocal laser endomicroscopy (pCLE) is a subcellular in vivo imaging technique capable of producing images that enable diagnosis of malign structural modifications in epithelial tissue. Images acquired with pCLE are, however, often tainted by significant artifacts that impair diagnosis. This is especially detrimental for automated image analysis, which is why said images are often excluded from recognition pipelines.

methodsWe present an approach for the automatic detection of motion artifacts in pCLE images and apply this methodology to a data set of 15 thousand images of epithelial tissue acquired in the oral cavity and the vocal folds. The approach is based on transfer learning from intermediate endpoints within a pre-trained Inception v3 network with tailored preprocessing. For detection within the non-rectangular pCLE images, we perform pooling within the activation maps of the network and evaluate this at different network depths.

resultsWe achieved area under the ROC curve values of 0.92 with the proposed method, compared to 0.80 for the best feature-based machine learning approach. Our overall accuracy with the presented approach is 94.8%.

conclusionOver traditional machine learning approaches with state-of-the-art features, we achieved significantly improved overall performance.

Indexed as

Deep LearningArtifactsEndoscopyHumansMicroscopy, ConfocalMotionConfocal laser endomicroscopyDeep convolutional neural networksMotion artifact detection

Identifiers

PMID30078151
OpenAlexW2885025700

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.