Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cluster Sampling Method

Cluster Sampling Method

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...

You might also read

Related Articles

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

Sort by

Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017

Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017

Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017

Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017

Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017

Same author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017

Search research articles

Related Experiment Video

Updated: Aug 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

ClusterSeg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets.

Jing Ke¹, Yizhou Lu², Yiqing Shen³

¹School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.

Medical Image Analysis

|February 2, 2023

Summary

This summary is machine-generated.

This study introduces ClusterSeg, a novel framework for segmenting clustered cell nuclei in biomedical images. It improves accuracy in challenging crowded cell images, offering a generalizable solution for various imaging types.

Keywords:

Clustered nuclei Microscope Nucleus segmentation Partially-supervised network Pathology

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Aug 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Area of Science:

Computational Biology
Medical Image Analysis
Computer Vision

Background:

Accurate cell and nuclei detection and segmentation are crucial for biomedical image analysis.
Challenges arise from crowded cell clusters with diverse morphologies, hindering instance segmentation.
Existing methods struggle with the precise delineation of individual nuclei within dense clusters.

Purpose of the Study:

To propose nuclei cluster-focused annotation strategies and frameworks to overcome limitations in cell instance segmentation.
To introduce ClusterSeg, a novel framework for precise nuclei instance segmentation, contour, and clustered-edge prediction.
To develop an annotation-efficient strategy for partially supervised segmentation of clustered nuclei.

Main Methods:

A convolutional-transformer hybrid encoder and a 2.5-path decoder form the core of the ClusterSeg framework.
An annotation-efficient clustered-edge pointed strategy is employed for salient and error-prone boundary identification.
A partially-supervised variant, PS-ClusterSeg, leverages ClusterSeg for enhanced segmentation with limited annotations.

Main Results:

ClusterSeg and PS-ClusterSeg demonstrate superior performance across multiple metrics on diverse datasets with severely clustered nuclei.
The proposed methods are modality-independent, showing generalizability across microscopy, cytopathology, and histopathology images.
Empirical evaluations confirm the framework's superiority over current state-of-the-art approaches in nuclei instance segmentation.

Conclusions:

ClusterSeg and PS-ClusterSeg effectively address the challenge of segmenting clustered nuclei in biomedical images.
The developed frameworks offer robust and generalizable solutions applicable to a wide range of imaging modalities.
The study provides valuable resources, including curated datasets, annotations, and source code, to facilitate further research.