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

Overview Of Cell Separation And Isolation

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

You might also read

Related Articles

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

Sort by

Same author

Biological significance, molecular mechanisms and clinical potential of EI24 in cancer.

Chinese medical journal·2025

Same author

Construction of a Heterotrophic Nitrification-Aerobic Denitrification Composite Microbial Consortium and Its Bioaugmentation Role in Wastewater Treatment.

Biology·2025

Same author

A distributed alternating optimization approach to canonical correlation analysis based fault detection for dynamic systems.

ISA transactions·2025

Same author

TPI1 enhances gemcitabine resistance in bladder cancer by promoting autophagy through activating Beclin-1.

Cell death & disease·2025

Same author

Targeting PSMB5-induced PANoptosis in bladder cancer: multi-omics insights and TCM candidate discovery.

Frontiers in immunology·2025

Same author

Dihydro-R demonstrates innate immunity against Adenovirus-7 by suppressing the NF-κB/JAK-STAT pathway in a SIRT1-dependent manner.

Biochemistry and biophysics reports·2025

Same journal

Wavelet-inspired diffusion model with near-field constraint for real-time echocardiography dehazing.

Medical image analysis·2026

Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026

Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026

Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026

Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 26, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Weakly supervised cell instance segmentation under various conditions.

Kazuya Nishimura¹, Chenyang Wang², Kazuhide Watanabe³

¹Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.

Medical Image Analysis

|August 2, 2021

Summary

This summary is machine-generated.

This study introduces a weakly supervised method for cell instance segmentation, significantly reducing annotation costs by using only cell centroid positions. The approach effectively segments cells across diverse conditions, outperforming existing weakly supervised techniques.

Keywords:

Cell segmentation Instances segmentation Weakly-supervised learning

More Related Videos

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

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Related Experiment Videos

Last Updated: Oct 26, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

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

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Area of Science:

Biomedical imaging
Computational biology
Machine learning in life sciences

Background:

Accurate cell instance segmentation is crucial for quantitative analysis in biomedical research.
Deep learning methods require extensive, condition-specific annotations, increasing time and labor costs.
Existing weakly supervised methods often struggle with diverse imaging conditions.

Purpose of the Study:

To develop a cost-effective weakly supervised method for cell instance segmentation.
To reduce the reliance on detailed, per-condition cell boundary annotations.
To enable robust cell segmentation across various microscopy and cell types.

Main Methods:

Proposed a weakly supervised deep learning approach for cell instance segmentation.
Utilized rough cell centroid positions as the primary training data.
Evaluated performance on diverse microscopy image datasets.
Demonstrated annotation-free segmentation using phase contrast and fluorescence image pairs.

Main Results:

The proposed method significantly reduced annotation costs compared to supervised approaches.
Achieved superior average performance over conventional weakly supervised methods.
Successfully performed cell instance segmentation without manual annotation in specific scenarios.

Conclusions:

Weakly supervised cell instance segmentation using centroid data is a viable and efficient alternative.
The method offers a practical solution for large-scale cell image analysis.
Potential for annotation-free segmentation broadens its applicability in biological research.