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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Dynamical multi-order responses and global semantic-infused adversarial learning: A robust airway segmentation

Sheng Zhang1, Yang Nan1, Yingying Fang1

  • 1Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK; National Heart and Lung Institute, Imperial College London, SW3 6LY, UK.

Medical Image Analysis
|November 21, 2025
PubMed
Summary

This study introduces a new airway segmentation model (DMGSA) that uses both unsupervised and supervised learning to improve accuracy in CT scans. The model effectively addresses challenges like limited data and image inconsistencies for better lung disease diagnosis.

Keywords:
Adversarial learningAirway segmentationCOVID-19Computerized tomographyOriented gradientsSupervised learningUnsupervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Automated airway segmentation in CT images is vital for diagnosing lung diseases.
  • Supervised learning is limited by scarce manual annotations, leading to segmentation issues.
  • Unconstrained intensities and sample imbalance cause discontinuity and false negatives in airway segmentation.

Purpose of the Study:

  • To propose a novel airway segmentation model, DMGSA, that integrates unsupervised and supervised learning to overcome label scarcity and improve accuracy.
  • To enhance the model's ability to perceive context and textural features of airways, particularly terminal bronchioles.
  • To validate the model's performance and robustness across diverse lung disease datasets.

Main Methods:

  • Developed a Dynamical Multi-order responses and Global Semantic-infused Adversarial network (DMGSA) combining parallel unsupervised and supervised learning branches.
  • Unsupervised branch features Dynamic Mask-Ratio (DMR) for varied context perception, Multi-Order Normalized Responses (MONR) for enhanced textural representation, and Adversarial Learning (AL) for feature discernment.
  • Supervised branch incorporates a Generalized Mean pooling based Global Semantic-infused (GMGS) module for improved robustness.

Main Results:

  • The proposed DMGSA model demonstrated superior performance in airway segmentation compared to state-of-the-art methods.
  • The model showed robustness when tested on datasets for lung cancer, COVID-19, and lung fibrosis.
  • Experimental results confirmed the effectiveness of the integrated unsupervised and supervised learning approach.

Conclusions:

  • The DMGSA model effectively alleviates label scarcity and improves airway segmentation accuracy in CT images.
  • The novel components, including DMR, MONR, AL, and GMGS, contribute to enhanced contextual understanding and textural feature extraction.
  • The method shows significant promise for accurate and robust automated airway segmentation in the diagnosis of various lung diseases.