Limits...
RandomSpot: A web-based tool for systematic random sampling of virtual slides.

Wright AI, Grabsch HI, Treanor DE - J Pathol Inform (2015)

Bottom Line: 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.Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications.

View Article: PubMed Central - PubMed

Affiliation: Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, England, UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Examples of hand drawn annotation types, drawn on the same digital slide-rectangular, elliptical and polygonal
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4355835&req=5

Figure 1: Examples of hand drawn annotation types, drawn on the same digital slide-rectangular, elliptical and polygonal

Mentions: Use case one utilizes HTML5 and JQuery to create a simple, user-friendly interface for uploading XML ROI. ROI can be rectangular, elliptical or polygonal [Figure 1]. Once uploaded, the RandomSpot algorithm places equidistant, systematic, randomly distributed spots within the ROIs.


RandomSpot: A web-based tool for systematic random sampling of virtual slides.

Wright AI, Grabsch HI, Treanor DE - J Pathol Inform (2015)

Examples of hand drawn annotation types, drawn on the same digital slide-rectangular, elliptical and polygonal
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4355835&req=5

Figure 1: Examples of hand drawn annotation types, drawn on the same digital slide-rectangular, elliptical and polygonal
Mentions: Use case one utilizes HTML5 and JQuery to create a simple, user-friendly interface for uploading XML ROI. ROI can be rectangular, elliptical or polygonal [Figure 1]. Once uploaded, the RandomSpot algorithm places equidistant, systematic, randomly distributed spots within the ROIs.

Bottom Line: 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.Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications.

View Article: PubMed Central - PubMed

Affiliation: Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, England, UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus