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Progressive curriculum learning with Scale-Enhanced U-Net for continuous airway segmentation.

Bingyu Yang1,2, Qingyao Tian1,2, Huai Liao3

  • 1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

BMC Medical Imaging
|December 17, 2025
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Summary

This study introduces a novel deep learning method for precise airway segmentation in CT scans, improving continuity and accuracy, especially for small airways, crucial for surgical planning and navigation.

Keywords:
Airway treeCT image segmentationCurriculum learningU-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonary Medicine

Background:

  • Accurate airway segmentation in chest CT is vital for clinical applications like surgical planning and bronchoscopy.
  • Deep learning methods face challenges in maintaining airway continuity due to class imbalance and blurred details.

Purpose of the Study:

  • To develop an advanced deep learning framework for continuous and accurate airway segmentation.
  • To enhance the detection and segmentation of small airways while preserving overall airway tree completeness.

Main Methods:

  • A progressive curriculum learning pipeline with three stages: coarse learning, General Union Loss (GUL), and Adaptive Topology-Responsive Loss (ATRL).
  • A Scale-Enhanced U-Net (SE-UNet) with multi-scale inputs and Detail Information Enhancers (DIEs).
  • Crop sampling strategy to address intra-class imbalance and robust airway tree parsing with hierarchical evaluation metrics.

Main Results:

  • The proposed method achieved superior performance on the ATM'22 challenge dataset.
  • Demonstrated a 9.631% improvement in Tree length Detection (TD) for small airways and a 4.622% improvement in Branch Detection (BD) on an in-house dataset.
  • Significantly enhanced small airway accuracy and overall airway tree completeness.

Conclusions:

  • The progressive curriculum learning pipeline and SE-UNet effectively address challenges in airway segmentation.
  • The framework offers improved accuracy and completeness for airway segmentation, beneficial for clinical practice.
  • The proposed method represents a significant advancement in automated airway analysis from CT images.