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

You might also read

Related Articles

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

Sort by
Same author

Green Fluorescent Protein GFP-Chromophore-Based Probe for the Detection of Mitochondrial Viscosity in Living Cells.

ACS applied bio materials·2022
Same author

Preparation of Zirconium Hydrogen Phosphate Coatings on Sandblasted/Acid-Etched Titanium for Enhancing Its Osteoinductivity and Friction/Corrosion Resistance.

International journal of nanomedicine·2022
Same author

https://www.fungiofpakistan.com: a continuously updated online database of fungi in Pakistan.

Database : the journal of biological databases and curation·2021
Same author

Phylogenetic Relationships, Speciation, and Origin of <i>Armillaria</i> in the Northern Hemisphere: A Lesson Based on rRNA and Elongation Factor 1-Alpha.

Journal of fungi (Basel, Switzerland)·2021
Same author

Production of Polyhydroxyalkanoates in Unsterilized Hyper-Saline Medium by Halophiles Using Waste Silkworm Excrement as Carbon Source.

Molecules (Basel, Switzerland)·2021
Same author

An efficient scRNA-seq dropout imputation method using graph attention network.

BMC bioinformatics·2021

Related Experiment Video

Updated: May 5, 2026

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
08:24

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Published on: May 15, 2016

7.9K

YoMacs: A high-precision and lightweight algorithm for mouse head-face segmentation.

Nani Jin1, Renjia Ye1, Lei Cai2

  • 1Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, 200444, China.

Computers in Biology and Medicine
|March 2, 2025
PubMed
Summary

This study introduces a lightweight AI model for precise mouse head-face segmentation, achieving 99.5% accuracy. This advancement aids in analyzing mouse behavior for biological and medical research.

Keywords:
Behavioral patternBiology and medicineMouse head-faceSemantic segmentationYolov8

More Related Videos

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans
08:47

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Published on: July 5, 2019

9.2K
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

785

Related Experiment Videos

Last Updated: May 5, 2026

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
08:24

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Published on: May 15, 2016

7.9K
Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans
08:47

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Published on: July 5, 2019

9.2K
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

785

Area of Science:

  • Computer Vision
  • Animal Behavior Analysis
  • Biomedical Research

Background:

  • Mice are vital experimental models in biology and medicine.
  • Behavioral patterns in mice offer insights into physical, mental, and neural states.
  • Accurate head-face segmentation is crucial for detailed behavioral analysis, yet studies are limited.

Purpose of the Study:

  • To develop a high-precision, lightweight algorithm for mouse head-face semantic segmentation.
  • To improve upon existing segmentation models by focusing on specific anatomical regions.
  • To provide a valuable tool for researchers studying mouse behavior and neuroscience.

Main Methods:

  • Proposed a novel, lightweight Yolov8-based algorithm for semantic segmentation.
  • Incorporated a bidirectional retention mechanism for efficient parallel inference.
  • Implemented dynamic feature weight allocation and a lightweight detection head with shared weights.
  • Utilized a custom dataset of 120 mouse head-face images available on Mendeley Data.

Main Results:

  • Achieved a segmentation accuracy of 99.5% for mouse head-faces.
  • Outperformed the original Yolov8 model in segmentation precision.
  • Demonstrated effective utilization of both local and global features.
  • Reduced computational load and parameter quantities through model optimization.

Conclusions:

  • The developed lightweight algorithm offers superior performance for mouse head-face segmentation.
  • This tool can significantly enhance the analysis of mouse behavior and neural mechanisms.
  • The study provides a valuable, optimized solution for a critical research need in biomedical science.