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

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

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

Sort by

Same author

The double-edged sword effects of differential leadership on deviant behavior.

Current psychology (New Brunswick, N.J.)·2022

Same author

Identification of the intersegmental plane by arterial ligation method during thoracoscopic segmentectomy.

Journal of cardiothoracic surgery·2022

Same author

AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels.

Frontiers in oncology·2022

Same author

Traumatic bone marrow lesions in dual-energy computed tomography.

Insights into imaging·2022

Same author

Core promoter in TNBC is highly mutated with rich ethnic signature.

Briefings in functional genomics·2022

Same author

Antitumor Activity of the Zinc Oxide Nanoparticles Coated with Low-Molecular-Weight Heparin and Doxorubicin Complex <i>In Vitro</i> and <i>In Vivo</i>.

Molecular pharmaceutics·2022

Search research articles

Related Experiment Video

Updated: Jul 3, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.

Zini Jian¹, Tianxiang Song¹, Zhihui Zhang¹

¹School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This study introduces SEAM-Unet++, an automated algorithm for analyzing Fluorescence in situ hybridization (FISH) images. It uses deep learning with an attention mechanism to accurately segment cells, improving diagnostic efficiency.

Keywords:

FISH images Unet++attention cell contours segmentation

More Related Videos

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Related Experiment Videos

Last Updated: Jul 3, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Area of Science:

Cytogenetics
Medical Imaging
Computational Biology

Background:

Fluorescence in situ hybridization (FISH) is crucial for detecting nucleic acid sequences in medical diagnostics and life sciences.
Analyzing FISH images is challenging due to large cell numbers and disorganized sequences, leading to time-consuming manual processing and potential errors.
Deep learning, particularly attention mechanisms, shows promise in enhancing medical image analysis by focusing on critical features.

Purpose of the Study:

To develop an automated algorithm for accurate cell contour segmentation in FISH images.
To address the limitations of manual FISH image analysis, including time consumption and potential for human error.
To improve the efficiency and accuracy of FISH image analysis through deep learning integration.

Main Methods:

Development of the SEAM-Unet++ algorithm, combining medical imaging with deep learning.
Integration of an attention mechanism within the U-Net++ architecture for enhanced feature focus.
Application of the algorithm to segment cell contours in complex FISH images.

Main Results:

The SEAM-Unet++ algorithm demonstrated improved accuracy in segmenting cell contours within FISH images.
The integrated attention mechanism enabled more efficient segmentation of adherent cells.
The automated approach significantly reduces the time and labor associated with FISH image analysis.

Conclusions:

The SEAM-Unet++ algorithm offers a robust solution for automated FISH image analysis.
The attention mechanism is key to improving segmentation accuracy, especially for challenging image datasets.
This deep learning-based approach enhances the utility of FISH in diagnostics and research by streamlining image interpretation.