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Related Experiment Video

Updated: Sep 6, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19

Camilla Risoli1, Marco Nicolò2, Davide Colombi1

  • 1Department of Radiological Function, "Guglielmo da Saliceto" Hospital, Via Taverna 49, 29121 Piacenza, Italy.

Diagnostics (Basel, Switzerland)
|June 24, 2022
PubMed

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Summary

Chest CT scans help assess COVID-19 pneumonia extent. AI software shows excellent reliability, with 3D Slicer closely matching radiologist visual scores for disease assessment.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pulmonology

Background:

  • Chest Computed Tomography (CT) is crucial for diagnosing COVID-19 pneumonia.
  • Qualitative (visual) and quantitative (AI-based) methods assess lung involvement extent.
  • This study compares these methods and evaluates AI software concordance.

Purpose of the Study:

  • To compare qualitative and quantitative pathological lung extension data in COVID-19 patients.
  • To assess the concordance of quantitative data from three different lung segmentation software.

Main Methods:

  • A double-center study of 120 COVID-19 patients with positive RT-PCR tests.
  • Retrospective analysis of CT scans by experienced radiologists (qualitative) and trained radiographers using three AI software (quantitative).
Keywords:
COVID-19 pneumoniachest CTlung segmentationpost-processing toolssemi-automatic segmentation software

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  • Software used: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D.

    • Main Results:

    • Good agreement between radiologists for visual estimation (ICC 0.79).
    • Excellent reliability among the three AI software (ICC 0.92).
    • 3D Slicer's "LungCTAnalyzer" showed the best agreement with the median radiologist visual score (ICC 0.75).

    Conclusions:

    • Direct comparison of qualitative (radiologist) and quantitative (AI) methods for COVID-19 lung involvement.
    • AI-based lung segmentation software demonstrates high reliability.
    • Quantitative CT data holds potential as prognostic and clinical course parameters.