Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A simple computer vision algorithm as a clinical aid for the pathologist

View ORCID ProfileJ.M. Lázaro-Guevara, B.J. Flores-Robles, A.E. Murga, K.M. Garrido
doi: https://doi.org/10.1101/853325
J.M. Lázaro-Guevara
aDepartment of Human Genetics, University of Utah, Salt Lake City, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for J.M. Lázaro-Guevara
  • For correspondence: jose.lazaroguevara@utah.edu
B.J. Flores-Robles
bRheumatology Department, San Pedro Hospital, Logroño, Spain
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
A.E. Murga
cComputer Sciences, Mariano Galvez University, Guatemala, Guatemala
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
K.M. Garrido
d, Guatemala
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Histological analysis for cancer detection or stratification is performed by observing and examining a small portion of a biopsied tissue under a microscope. Nevertheless, to assign clinical meaning to the findings, the analysis and interpretation of an experienced Pathologist is always necessary. Using high-resolution images, these experts visually examine the sample looking for specific characteristics on the cell shapes and tissue distributions, so they could decide whether tissue regions are cancerous, and establish the malignancy level of it. However, with the increasing demand for work for those pathologists and the importance of accuracy on diagnostics, multiple attempts to simplify their work have been performed. Current Imaging technologies allow novel horizons in the automatized selection of some of the characteristics that indicate malignancy in a biopsy. In this work, we propose a simple computer vision algorithm that can be implemented as a screening method for focusing in histological areas with higher risk of malignancy saving time to the pathologist and helping to perform a more standardized work, an easy observation with the potential to become in an aid to daily clinical work.

Footnotes

  • bjflores{at}riojasalud.es, dra.kmgarrido{at}gmail.com

  • https://github.com/megahitokiri/A-simple-computer-vision-algorithm-as-a-clinical-aid-for-the-pathologist

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted November 25, 2019.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A simple computer vision algorithm as a clinical aid for the pathologist
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A simple computer vision algorithm as a clinical aid for the pathologist
J.M. Lázaro-Guevara, B.J. Flores-Robles, A.E. Murga, K.M. Garrido
bioRxiv 853325; doi: https://doi.org/10.1101/853325
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A simple computer vision algorithm as a clinical aid for the pathologist
J.M. Lázaro-Guevara, B.J. Flores-Robles, A.E. Murga, K.M. Garrido
bioRxiv 853325; doi: https://doi.org/10.1101/853325

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3598)
  • Biochemistry (7564)
  • Bioengineering (5517)
  • Bioinformatics (20779)
  • Biophysics (10316)
  • Cancer Biology (7973)
  • Cell Biology (11629)
  • Clinical Trials (138)
  • Developmental Biology (6602)
  • Ecology (10197)
  • Epidemiology (2065)
  • Evolutionary Biology (13605)
  • Genetics (9537)
  • Genomics (12843)
  • Immunology (7919)
  • Microbiology (19536)
  • Molecular Biology (7653)
  • Neuroscience (42054)
  • Paleontology (307)
  • Pathology (1257)
  • Pharmacology and Toxicology (2200)
  • Physiology (3266)
  • Plant Biology (7036)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1951)
  • Systems Biology (5426)
  • Zoology (1115)