Evidence map›Paper›PMID 37765731›Full record

ArticleSensors (Basel, Switzerland)2023

Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy.

Vincenzo Taormina, Giuseppe Raso, Vito Gentile, Leonardo Abbene, Antonino Buttacavoli, Gaetano Bonsignore, Cesare Valenti, Pietro Messina, Giuseppe Alessandro Scardina, Donato Cascio

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

10 authors at 1 institution in 1 country.

Vincenzo TaorminaDepartment of Mathematics and Informatics, University of Palermo, 90128 Palermo, Italy.ORCID 0000-0002-8313-2556
Giuseppe RasoDepartment of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.
Vito GentileDepartment of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.ORCID 0000-0002-3844-3136
Leonardo AbbeneDepartment of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.
Antonino ButtacavoliDepartment of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.
Gaetano BonsignoreDepartment of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.
Cesare ValentiDepartment of Mathematics and Informatics, University of Palermo, 90128 Palermo, Italy.ORCID 0000-0002-4961-2054
Pietro MessinaDepartment of Surgical Oncological and Stomatological Disciplines, University of Palermo, 90127 Palermo, Italy.
Giuseppe Alessandro ScardinaDepartment of Surgical Oncological and Stomatological Disciplines, University of Palermo, 90127 Palermo, Italy.ORCID 0000-0001-7305-530X
Donato CascioDepartment of Physics and Chemistry, University of Palermo, 90128 Palermo, Italy.ORCID 0000-0001-6522-1259
University of Palermo · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic-pathological lesions playing a crucial role in their progression. Despite its importance, the utilization of videocapillaroscopy in the oral cavity encounters limitations due to the acquisition setup, encompassing spatial and temporal resolutions of the video camera, objective magnification, and physical probe dimensions. Moreover, the operator's influence during the acquisition process, particularly how the probe is maneuvered, further affects its effectiveness. This study aims to address these challenges and improve data reliability by developing a computerized support system for microcirculation analysis. The designed system performs stabilization, enhancement and automatic segmentation of capillaries in oral mucosal video sequences. The stabilization phase was performed by means of a method based on the coupling of seed points in a classification process. The enhancement process implemented was based on the temporal analysis of the capillaroscopic frames. Finally, an automatic segmentation phase of the capillaries was implemented with the additional objective of quantitatively assessing the signal improvement achieved through the developed techniques. Specifically, transfer learning of the renowned U-net deep network was implemented for this purpose. The proposed method underwent testing on a database with ground truth obtained from expert manual segmentation. The obtained results demonstrate an achieved Jaccard index of 90.1% and an accuracy of 96.2%, highlighting the effectiveness of the developed techniques in oral capillaroscopy. In conclusion, these promising outcomes encourage the utilization of this method to assist in the diagnosis and monitoring of conditions that impact microcirculation, such as rheumatologic or cardiovascular disorders.

Indexed as

CapillariesCardiovascular DiseasesHumansImage Processing, Computer-AssistedMicroscopic AngioscopyReproducibility of ResultsVeinscapillaries segmentationcapillaroscopydeep learningmicrocirculationsignal enhancementU-netvideo stabilization

Identifiers

PMID37765731
PMCPMC10536112
OpenAlexW4386444584

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.