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Multi-Scale Fusion for Real-Time Image Observation and Data Analysis of Athletes after Soft Tissue Injury.

Jinhui Li1, Yang Yu2, Jiaxing Han1

  • 1College of Physical Education, Qiqihar University, Qiqihar 161006, Heilongjiang, China.

Current Medical Imaging
|October 21, 2025
PubMed
Summary

This study introduces an improved Swin-Unet model for athlete soft tissue injury segmentation, achieving higher accuracy and significantly reducing diagnosis time for better clinical applications.

Keywords:
Athlete InjuriesImage Segmentation.Medical ImagingMulti-Scale FusionSoft Tissue Injury

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Accurate segmentation of soft tissue injuries in athletes is crucial for effective diagnosis and treatment.
  • Existing segmentation models often face challenges with accuracy and efficiency in complex medical images.

Purpose of the Study:

  • To enhance the segmentation accuracy of athlete soft tissue injuries.
  • To develop a more efficient and clinically useful tool for medical image analysis.

Main Methods:

  • An enhanced Swin-Unet model incorporating Feature Pyramid Network (FPN) for multiscale feature fusion.
  • An adaptive window selection mechanism for dynamic receptive field adjustment.
  • A weighted hybrid loss function (Dice Loss, Cross-Entropy Loss, boundary auxiliary loss) for precise segmentation and boundary recognition.

Main Results:

  • Achieved a Dice Similarity Coefficient (DSC) of 0.978 on the OAI-ZIB dataset.
  • Demonstrated superior performance in Intersection over Union (IoU) (0.968) and boundary Hausdorff distance (3.21) compared to baseline models.
  • Significantly reduced manual diagnosis time from 16.8 minutes to 6.0 minutes.

Conclusions:

  • The proposed framework significantly improves soft tissue segmentation accuracy and efficiency in athlete injury analysis.
  • The enhanced model offers improved clinical utility for real-time medical imaging analysis.
  • This approach represents a step forward in automated diagnostic tools for sports-related injuries.