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RandomSpot: A web-based tool for systematic random sampling of virtual slides
This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Systematic random sampling (SRS) is a stereological tool, which provides a framework to quickly build an accurate estimation of the distribution of objects or classes within an image, whilst min...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4355835/ https://www.ncbi.nlm.nih.gov/pubmed/25774319 http://dx.doi.org/10.4103/2153-3539.151906 |
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author | Wright, Alexander I. Grabsch, Heike I. Treanor, Darren E. |
author_facet | Wright, Alexander I. Grabsch, Heike I. Treanor, Darren E. |
author_sort | Wright, Alexander I. |
collection | PubMed |
description | This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Systematic random sampling (SRS) is a stereological tool, which provides a framework to quickly build an accurate estimation of the distribution of objects or classes within an image, whilst minimizing the number of observations required. RandomSpot is a web-based tool for SRS in stereology, which systematically places equidistant points within a given region of interest on a virtual slide. Each point can then be visually inspected by a pathologist in order to generate an unbiased sample of the distribution of classes within the tissue. Further measurements can then be derived from the distribution, such as the ratio of tumor to stroma. RandomSpot replicates the fundamental principle of traditional light microscope grid-shaped graticules, with the added benefits associated with virtual slides, such as facilitated collaboration and automated navigation between points. Once the sample points have been added to the region(s) of interest, users can download the annotations and view them locally using their virtual slide viewing software. Since its introduction, RandomSpot has been used extensively for international collaborative projects, clinical trials and independent research projects. So far, the system has been used to generate over 21,000 sample sets, and has been used to generate data for use in multiple publications, identifying significant new prognostic markers in colorectal, upper gastro-intestinal and breast cancer. Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications. |
format | Online Article Text |
id | pubmed-4355835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-43558352015-03-13 RandomSpot: A web-based tool for systematic random sampling of virtual slides Wright, Alexander I. Grabsch, Heike I. Treanor, Darren E. J Pathol Inform Symposium - 2nd Nordic Symposium on Digital Pathology This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Systematic random sampling (SRS) is a stereological tool, which provides a framework to quickly build an accurate estimation of the distribution of objects or classes within an image, whilst minimizing the number of observations required. RandomSpot is a web-based tool for SRS in stereology, which systematically places equidistant points within a given region of interest on a virtual slide. Each point can then be visually inspected by a pathologist in order to generate an unbiased sample of the distribution of classes within the tissue. Further measurements can then be derived from the distribution, such as the ratio of tumor to stroma. RandomSpot replicates the fundamental principle of traditional light microscope grid-shaped graticules, with the added benefits associated with virtual slides, such as facilitated collaboration and automated navigation between points. Once the sample points have been added to the region(s) of interest, users can download the annotations and view them locally using their virtual slide viewing software. Since its introduction, RandomSpot has been used extensively for international collaborative projects, clinical trials and independent research projects. So far, the system has been used to generate over 21,000 sample sets, and has been used to generate data for use in multiple publications, identifying significant new prognostic markers in colorectal, upper gastro-intestinal and breast cancer. Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications. Medknow Publications & Media Pvt Ltd 2015-02-24 /pmc/articles/PMC4355835/ /pubmed/25774319 http://dx.doi.org/10.4103/2153-3539.151906 Text en Copyright: © 2015 Wright AI. http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Symposium - 2nd Nordic Symposium on Digital Pathology Wright, Alexander I. Grabsch, Heike I. Treanor, Darren E. RandomSpot: A web-based tool for systematic random sampling of virtual slides |
title | RandomSpot: A web-based tool for systematic random sampling of virtual slides |
title_full | RandomSpot: A web-based tool for systematic random sampling of virtual slides |
title_fullStr | RandomSpot: A web-based tool for systematic random sampling of virtual slides |
title_full_unstemmed | RandomSpot: A web-based tool for systematic random sampling of virtual slides |
title_short | RandomSpot: A web-based tool for systematic random sampling of virtual slides |
title_sort | randomspot: a web-based tool for systematic random sampling of virtual slides |
topic | Symposium - 2nd Nordic Symposium on Digital Pathology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4355835/ https://www.ncbi.nlm.nih.gov/pubmed/25774319 http://dx.doi.org/10.4103/2153-3539.151906 |
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