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DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.

Cheng Chen1, Jiancang Zhou2, Kangneng Zhou1

  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

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|November 27, 2021
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Summary

This study developed a 3D deep learning method to accurately segment COVID-19 lung infections from CT scans. The advanced technique improves infection detection for better quantitative analysis of the disease.

Keywords:
3D convolutional neural networkCOVID-19data enhancementinfection segmentationweighted loss function

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • The COVID-19 pandemic necessitated advanced diagnostic tools for analyzing lung infections.
  • Computed Tomography (CT) scans are crucial for visualizing COVID-19 related lung abnormalities.
  • Accurate segmentation of infected lung regions is vital for disease assessment and management.

Purpose of the Study:

  • To develop and validate a novel 3D deep learning model for segmenting COVID-19 lung infections from CT images.
  • To enhance the accuracy of infection detection through advanced image processing and a refined neural network architecture.
  • To provide a reliable computational tool for quantitative analysis of COVID-19 in CT scans.

Main Methods:

  • Lung regions were extracted and enhanced using voxel gradient analysis to capture geometric characteristics.
  • A deep weighted UNet model with a weighted loss function was employed for refining 3D infection texture.
  • The model's performance was evaluated using Accuracy, Precision, Recall, and Coincidence Rate on private and public datasets.

Main Results:

  • The proposed method demonstrated high performance on a private dataset (e.g., 99.94% Accuracy) and a public Kaggle dataset (e.g., 99.73% Accuracy).
  • Statistical tests confirmed significant differences between various tested models, validating the effectiveness of the proposed approach.
  • The study achieved strong results in segmenting 3D COVID-19 lung infections, indicating robustness and accuracy.

Conclusions:

  • This research presents an effective 3D segmentation technology for COVID-19 infections in CT images.
  • The developed method serves as a crucial prerequisite for quantitative analysis of COVID-19 in radiological imaging.
  • The findings support the integration of AI-driven tools for improved COVID-19 diagnosis and monitoring.