Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Random Sampling Method01:09

Random Sampling Method

15.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.9K
Stratified Sampling Method01:16

Stratified Sampling Method

16.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
16.5K
Systematic Sampling Method01:17

Systematic Sampling Method

14.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
14.2K
Cluster Sampling Method01:20

Cluster Sampling Method

15.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.7K
Randomized Experiments01:13

Randomized Experiments

9.4K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.4K
Sampling Plans01:23

Sampling Plans

1.3K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The clinical relevance of regional lymph node microarchitecture in oesophageal cancer patients - Results from the UK MRC OE02 trial.

European journal of cancer (Oxford, England : 1990)·2026
Same author

Impact of neoadjuvant chemotherapy on local breast microbiota and macrophages in ER-positive breast cancer with links to treatment response.

NPJ breast cancer·2026
Same author

Energy Balance-Related Factors and Direct Tumor-Adipocyte Contact in Colorectal Cancer: Etiology Insights from the Population-Based Netherlands Cohort Study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

The density of tumour infiltrating lymphocytes in oesophago-gastric cancer varies with disease stage, geographical region and treatment: a post hoc analysis of nine phase III clinical trials.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association·2026
Same author

Management of gastric cancer peritoneal metastasis: International Gastric Cancer Association GCPM Working Group consensus statements.

The British journal of surgery·2026
Same author

Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response.

The Journal of pathology·2026

Related Experiment Video

Updated: Apr 16, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.8K

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

Alexander I Wright1, Heike I Grabsch2, Darren E Treanor3

  • 1Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, England, UK.

Journal of Pathology Informatics
|March 17, 2015
PubMed
Summary
This summary is machine-generated.

Systematic random sampling (SRS) with the RandomSpot tool enables accurate digital pathology image analysis. This method efficiently estimates tissue features, aiding in identifying prognostic markers and training AI algorithms.

Keywords:
Random samplingrandomspotsystematicweb system

More Related Videos

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy
07:27

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Published on: May 13, 2012

17.4K
Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
09:33

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Published on: May 3, 2018

8.5K

Related Experiment Videos

Last Updated: Apr 16, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.8K
Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy
07:27

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Published on: May 13, 2012

17.4K
Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
09:33

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Published on: May 3, 2018

8.5K

Area of Science:

  • Digital Pathology
  • Stereology
  • Computational Pathology

Background:

  • Systematic random sampling (SRS) is a stereological method for estimating feature distribution in images.
  • Traditional methods using graticules are time-consuming and lack digital integration.
  • Virtual slides offer new possibilities for image analysis and collaboration.

Purpose of the Study:

  • To introduce RandomSpot, a web-based tool for performing SRS on virtual slides.
  • To demonstrate the utility of RandomSpot in digital pathology workflows.
  • To highlight its application in research, clinical trials, and AI development.

Main Methods:

  • RandomSpot systematically places equidistant points on a region of interest in a virtual slide.
  • Pathologists visually inspect each point to classify tissue components.
  • Annotations are downloadable for local analysis and integration with virtual slide viewing software.

Main Results:

  • RandomSpot has been used extensively in international projects, generating over 21,000 sample sets.
  • Data generated has identified new prognostic markers in colorectal, gastro-intestinal, and breast cancers.
  • The system provides valuable data for training image analysis algorithms.

Conclusions:

  • RandomSpot facilitates efficient and unbiased sampling for digital pathology.
  • It enhances collaboration and data sharing in research and clinical settings.
  • The tool supports the advancement of AI in pathology through annotated datasets.