Evidence map›Paper›PMID 28083826›Full record

ArticleJournal of digital imaging2017

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

Spiros Kostopoulos, Panagiota Ravazoula, Pantelis Asvestas, Ioannis Kalatzis, George Xenogiannopoulos, Dionisis Cavouras, Dimitris Glotsos

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.5field-weighted citation impact, top 14% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Spiros KostopoulosMedical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece.
Panagiota RavazoulaDepartment of Pathology, University Hospital of Patras, Rio, 265 04, Patras, Greece.
Pantelis AsvestasMedical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece.
Ioannis KalatzisMedical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece.
George XenogiannopoulosMedical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece.
Dionisis CavourasMedical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece.
Dimitris GlotsosMedical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece. dimglo@teiath.gr.
Technological Educational Institute of Athens · GRGeneral University Hospital of Patras · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

Indexed as

Decision Support Systems, ClinicalImage Processing, Computer-AssistedBrain NeoplasmsBreast NeoplasmsDiagnosis, Computer-AssistedExpert SystemsFemaleHumansLaryngeal NeoplasmsMaleMiddle AgedObserver VariationSoftwareBrain cancerBreast cancerCancerComputer-aided diagnosisHistology image collectionLaryngeal cancerMicroscopy

Identifiers

PMID28083826
PMCPMC5422235
OpenAlexW2574268335

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.