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Related Experiment Video

Updated: Jun 16, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

Dynamic U-shaped convolutional network for mouse cardiac image segmentation and quantification.

Yu Wang1, Wenwen Zhang2, Wanjun Zhang2

  • 1School of Physical Education, Henan University, Kaifeng, Henan, China.

Frontiers in Medicine
|June 15, 2026
PubMed
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This study introduces a novel dynamic U-shaped network for segmenting mouse heart images with myocardial infarction. The new method improves accuracy in identifying damaged areas, aiding cardiovascular disease research.

Area of Science:

  • Biomedical imaging
  • Cardiovascular research
  • Medical image analysis

Background:

  • Accurate segmentation of myocardial infarction in mouse cardiac images is crucial for cardiovascular disease research.
  • Manual segmentation is laborious, driving the need for automated methods.
  • Irregular U-shaped structures in infarcted areas present a segmentation challenge.

Purpose of the Study:

  • To develop an automated method for segmenting and quantifying mouse cardiac slice images with myocardial infarction.
  • To address the complexity of segmenting irregular U-shaped infarcted regions.
  • To improve the accuracy and efficiency of infarct size assessment.

Main Methods:

  • Proposed a dynamic U-shaped convolutional network (DU-Net) tailored for irregular structures.
Keywords:
U-shape convolutionalcardiac segmentationcardiac slice imagemouse cardiacmyocardial infarction

Related Experiment Videos

Last Updated: Jun 16, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

  • Incorporated dynamic convolution for focusing on U-shaped local features.
  • Utilized a dual-stream fusion block and an attention gate mechanism for enhanced performance.
  • Created and utilized a dataset of 243 mouse cardiac slice images with myocardial infarction.

    • Main Results:

    • The proposed DU-Net achieved an average Dice coefficient of 80.68%, outperforming existing algorithms by 1.7%.
    • For infarct size segmentation, the method reached 80.13%, surpassing the previous optimal by 2.43%.
    • The model demonstrated capability in quantifying the ratio of infarcted to risk areas.

    Conclusions:

    • The dynamic U-shaped network effectively segments and quantifies myocardial infarction in mouse cardiac images.
    • This automated approach offers improved accuracy and efficiency over existing methods.
    • The developed method aids in assessing myocardial injury severity and advances cardiovascular research.