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Updated: Sep 27, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model.

Sania Shamim1, Mazhar Javed Awan1, Azlan Mohd Zain2

  • 1Department of Software Engineering, University of Management and Technology, Lahore, Pakistan.

Journal of Healthcare Engineering
|April 15, 2022
PubMed
Summary

A novel segmentation method, convUnet, accurately identifies ground glass opacity in COVID-19 CT scans. This AI approach offers a faster, more reliable alternative to RT-PCR testing for diagnosing coronavirus disease 2019.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • The COVID-19 pandemic caused widespread health impacts globally.
  • Computed Tomography (CT) scans offer a potential alternative to RT-PCR for COVID-19 diagnosis.
  • Segmenting early-stage COVID-19 lung abnormalities like ground glass opacity (GGO) in CT scans is challenging due to their subtle appearance.

Purpose of the Study:

  • To develop an automated segmentation approach for identifying ground glass opacity (GGO) in COVID-19 CT images.
  • To improve the accuracy and efficiency of COVID-19 diagnosis using medical imaging analysis.
  • To propose a modified Unet model, termed convUnet, for precise pixel-level classification of ROIs in lung CT scans.

Main Methods:

  • A modified Unet deep learning model (convUnet) was developed, incorporating increased weights in its contracting and expanding paths.
  • An improved convolutional module was integrated to enhance connectivity between the encoder and decoder pipelines.
  • The convUnet model was trained and evaluated on the Medseg1 dataset for GGO segmentation.

Main Results:

  • The convUnet model demonstrated superior performance in segmenting GGO in COVID-19 CT images compared to standard Unet and other state-of-the-art models.
  • Quantitative metrics including accuracy (93.29%), recall (93.01%), precision (93.67%), Dice-coefficient (92.46%), F1-score (93.34%), and IOU (86.96%) were achieved.
  • The enhanced model effectively addressed the challenge of segmenting subtle GGO in early-stage COVID-19 infections.

Conclusions:

  • The proposed convUnet segmentation approach is accurate, fast, and reliable for diagnosing COVID-19 from CT scans.
  • This AI-driven method can significantly aid clinicians in the rapid and efficient diagnosis of coronavirus disease 2019.
  • The modifications to the Unet architecture enhance its capability for medical image segmentation tasks.