Evidence map›Paper›PMID 27505420›Full record

ArticlePloS one2016

Automated Texture Analysis and Determination of Fibre Orientation of Heart Tissue: A Morphometric Study.

Bernhard Zach, Ernst Hofer, Martin Asslaber, Helmut Ahammer

Abstract read
In one paragraph

Article in PloS one, 2016. 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. 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

4 authors.

Bernhard ZachInstitute of Biophysics, Centre for Physiological Medicine, Medical University of Graz, Harrachgasse 21, A-8010, Graz, Austria.ORCID http://orcid.org/0000-0003-0852-7889
Ernst HoferInstitute of Biophysics, Centre for Physiological Medicine, Medical University of Graz, Harrachgasse 21, A-8010, Graz, Austria.
Martin AsslaberInstitute of Pathology, Medical University of Graz, Auenbruggerplatz 25, A-8036, Graz, Austria.
Helmut AhammerInstitute of Biophysics, Centre for Physiological Medicine, Medical University of Graz, Harrachgasse 21, A-8010, Graz, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human heart has a heterogeneous structure, which is characterized by different cell types and their spatial configurations. The physical structure, especially the fibre orientation and the interstitial fibrosis, determines the electrical excitation and in further consequence the contractility in macroscopic as well as in microscopic areas. Modern image processing methods and parameters could be used to describe the image content and image texture. In most cases the description of the texture is not satisfying because the fibre orientation, detected with common algorithms, is biased by elements such as fibrocytes or endothelial nuclei. The goal of this work is to figure out if cardiac tissue can be analysed and classified on a microscopic level by automated image processing methods with a focus on an accurate detection of the fibre orientation. Quantitative parameters for identification of textures of different complexity or pathological attributes inside the heart were determined. The focus was set on the detection of the fibre orientation, which was calculated on the basis of the cardiomyocytes' nuclei. It turned out that the orientation of these nuclei corresponded with a high precision to the fibre orientation in the image plane. Additionally, these nuclei also indicated very well the inclination of the fibre.

Indexed as

AlgorithmsAnimalsAutomationImage Processing, Computer-AssistedMyocardiumMyocytes, CardiacRabbits

Identifiers

PMID27505420
PMCPMC4978441

What OpenQuestion holds

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Registered trials

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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.