Evidence map›Paper›PMID 33948761›Full record

ArticleJournal of digital imaging2021

Automatic Segmentation of Bone Canals in Histological Images.

Pedro Henrique Campos Cunha Gondim, Pedro Henrique Justino Oliveira Limirio, Flaviana Soares Rocha, Jonas Dantas Batista, Paula Dechichi, Bruno Augusto Nassif Travençolo, André Ricardo Backes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Pedro Henrique Campos Cunha GondimSchool of Computer Science, Federal University of Uberlândia, Uberlândia, Brazil.
Pedro Henrique Justino Oliveira LimirioFaculty of Dentistry, Federal University of Uberlândia, Uberlândia, Brazil.
Flaviana Soares RochaFaculty of Dentistry, Federal University of Uberlândia, Uberlândia, Brazil.
Jonas Dantas BatistaFaculty of Dentistry, Federal University of Uberlândia, Uberlândia, Brazil.
Paula DechichiBiomedical Science Institute, Federal University of Uberlândia, Uberlândia, Brazil.
Bruno Augusto Nassif TravençoloSchool of Computer Science, Federal University of Uberlândia, Uberlândia, Brazil.
André Ricardo BackesSchool of Computer Science, Federal University of Uberlândia, Uberlândia, Brazil. arbackes@yahoo.com.br.ORCID 0000-0002-7486-4253

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The literature provides many works that focused on cell nuclei segmentation in histological images. However, automatic segmentation of bone canals is still a less explored field. In this sense, this paper presents a method for automatic segmentation approach to assist specialists in the analysis of the bone vascular network. We evaluated the method on an image set through sensitivity, specificity and accuracy metrics and the Dice coefficient. We compared the results with other automatic segmentation methods (neighborhood valley emphasis (NVE), valley emphasis (VE) and Otsu). Results show that our approach is proved to be more efficient than comparable methods and a feasible alternative to analyze the bone vascular network.

Indexed as

AlgorithmsImage Processing, Computer-AssistedArtifact removalBone canalBone vascular networkImage segmentationMathematical morphology

Identifiers

PMID33948761
PMCPMC8329125

What OpenQuestion holds

Textmetadata
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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.