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Related Experiment Video

Updated: Aug 13, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification.

Mohammad Hamid Asnawi1, Anindya Apriliyanti Pravitasari1, Gumgum Darmawan1

  • 1Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung 45363, Indonesia.

Healthcare (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

The 3D UNet model achieved high accuracy in segmenting lungs and COVID-19 infections from CT scans. This AI-driven approach aids in faster diagnosis and assessing disease severity.

Keywords:
3D DenseUNet3D ResUNet3D UNet3D VGGUNet3D image segmentationCOVID-19 CT-scan

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • COVID-19, declared a Public Health Emergency of International Concern, necessitates rapid and accurate diagnostic tools.
  • Chest radiography, including CT scans, is crucial for COVID-19 diagnosis, but effective segmentation of affected areas is challenging.
  • Existing segmentation algorithms require evaluation for their efficacy in segmenting lung and infection regions in COVID-19 CT scans.

Purpose of the Study:

  • To evaluate the performance of the 3D UNet architecture and its modifications (3D ResUNet, 3D VGGUNet, 3D DenseUNet) for segmenting lung and infection areas in COVID-19 CT scans.
  • To compare the effectiveness of binary-class (lung segmentation) and multi-class (lung and infection segmentation) approaches using these 3D segmentation models.
  • To determine the optimal 3D segmentation model for improving COVID-19 diagnosis and severity assessment.

Main Methods:

  • CT scan datasets were preprocessed using min-max scaling and Contrast Limited Adaptive Histogram Equalization (CLAHE).
  • Four 3D deep learning segmentation architectures were implemented: 3D UNet, 3D ResUNet, 3D VGGUNet, and 3D DenseUNet.
  • Model performance was evaluated on both binary-class (lung segmentation) and multi-class (lung and infection segmentation) tasks using IoU, Dice scores, and accuracy metrics.

Main Results:

  • The original 3D UNet model outperformed the modified architectures in both binary and multi-class segmentation tasks.
  • For binary-class segmentation, 3D UNet achieved IoU of 94.32%, Dice score of 97.05%, and accuracy of 99.37%.
  • For multi-class segmentation, 3D UNet achieved IoU of 81.58%, Dice score of 88.61%, and accuracy of 98.78%, demonstrating its effectiveness in segmenting both lung and infection areas.

Conclusions:

  • The 3D UNet architecture demonstrates superior performance for segmenting lung and COVID-19 infection regions in CT scans compared to its modifications.
  • Accurate 3D segmentation of infection areas can significantly aid medical personnel in diagnosing COVID-19 and assessing disease severity.
  • Further development and application of 3D UNet for medical image analysis hold promise for improving diagnostic workflows in infectious diseases.