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Updated: Oct 10, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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A coarse-refine segmentation network for COVID-19 CT images.

Ziwang Huang1, Liang Li2, Xiang Zhang3

  • 1School of Data and Computer Science Sun Yat-Sen University Guangzhou China.

IET Image Processing
|December 13, 2021
PubMed
Summary

A new coarse-refine segmentation network accurately segments COVID-19 lung CT scans. This AI model improves boundary detection and multi-scale analysis for better infection assessment and treatment planning.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Accurate segmentation of COVID-19 infected regions in CT scans is crucial for effective patient treatment.
  • Challenges include multi-scale infected areas and unclear boundaries due to low contrast in CT images.

Purpose of the Study:

  • To develop an advanced segmentation network for precise COVID-19 CT image analysis.
  • To address limitations of existing models in segmenting complex infected lung regions.

Main Methods:

  • Proposed a novel coarse-refine segmentation network incorporating an atrous spatial pyramid pooling module.
  • Utilized a hybrid loss function to enhance boundary definition and capture delicate structures.
  • Implemented a coarse-refine architecture to improve segmentation accuracy.

Main Results:

  • The proposed network demonstrated superior performance in segmenting COVID-19 CT images compared to established medical segmentation models.
  • The model effectively handled multi-scale infected regions and improved boundary delineation.
  • Achieved more accurate estimates of infection progression.

Conclusions:

  • The developed coarse-refine segmentation network offers a significant advancement for COVID-19 diagnosis and treatment planning.
  • Enhanced segmentation accuracy facilitates better clinical decision-making for COVID-19 patients.
  • This AI-driven approach aids in providing more reasonable and effective treatment options.