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A full-scale lung image segmentation algorithm based on hybrid skip connection and attention mechanism.

Qiong Zhang1,2, Byungwon Min3, Yiliu Hang4

  • 1College of Computer and Information Engineering, Nantong Institute of Technology, Nantong, China. 18862928127@163.com.

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|October 5, 2024
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Summary

This study introduces a novel lung image segmentation algorithm (HAFS) that enhances accuracy by addressing background occlusion. The hybrid approach significantly improves key performance metrics for clearer lung image analysis.

Keywords:
Attention gateFeature fusionHybrid skip connectionLung image segmentationYolov8

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Lung image segmentation is crucial for diagnosing respiratory conditions.
  • Segmentation accuracy is often compromised by background object occlusion in medical images.
  • Existing algorithms struggle with complex background interferences.

Purpose of the Study:

  • To develop a robust full-scale lung image segmentation algorithm.
  • To improve segmentation accuracy by mitigating background occlusion effects.
  • To enhance feature fusion and important feature weighting for better lung delineation.

Main Methods:

  • Proposed a hybrid skip connection and attention mechanism (HAFS) algorithm.
  • Utilized YOLOv8 as the foundational network architecture.
  • Implemented multi-layer feature fusion with dense and sparse skip connections and attention gates.

Main Results:

  • The HAFS algorithm demonstrated improved precision, recall, and pixel accuracy.
  • Achieved superior Dice, mIoU, and mAP scores compared to existing methods.
  • Showcased enhanced GFLOPs metrics, indicating computational efficiency.

Conclusions:

  • The proposed HAFS algorithm effectively addresses background occlusion in lung image segmentation.
  • The hybrid approach significantly enhances segmentation performance and accuracy.
  • This method offers a promising advancement for clinical lung image analysis.