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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Related Experiment Video

Updated: Oct 5, 2025

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

Published on: December 19, 2020

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COVID-19 CT Scan Lung Segmentation: How We Do It.

Davide Negroni1, Domenico Zagaria2, Andrea Paladini2

  • 1Department of Radiology, "Maggiore Della Carità" Hospital, AOU Maggiore Della Carità, Corso Mazzini 18, Novara, Italy. dvdngr@gmail.com.

Journal of Digital Imaging
|January 29, 2022
PubMed
Summary

Quantitative analysis of COVID-19 pneumonia on chest CT using 3D Slicer can help manage patients. This open-source method segments lung parenchyma, aiding clinicians in assessing disease severity and guiding treatment decisions for better patient outcomes.

Keywords:
COVID-19Image processing, computer-assistedLung volume measurementsPneumoniaStandardsTomography, spiral computed

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Area of Science:

  • Medical Imaging
  • Pulmonology
  • Health Informatics

Background:

  • COVID-19 significantly strains healthcare systems, with a notable percentage of patients requiring hospitalization and intensive care.
  • Chest CT scans are crucial for assessing COVID-19 prognosis, correlating disease severity with lung parenchyma alterations.
  • Existing commercial software is costly and inaccessible, while open-source tools often lack standardization and are time-consuming.

Purpose of the Study:

  • To evaluate the effectiveness of an open-source, threshold-based segmentation method on 3D Slicer for analyzing lung parenchyma alterations in COVID-19 pneumonia.
  • To establish objective quantitative measures for different lung tissue conditions (aerated, interstitial, consolidated) in COVID-19 patients.
  • To assess the potential of this method in aiding clinical management and patient care.

Main Methods:

  • Analysis of chest CT exams from 246 suspected COVID-19 patients using 3D Slicer's "Segment Editor" and "Segment Quantification" tools.
  • Application of a threshold-based method for lung parenchyma segmentation, defining specific Hounsfield Unit (HU) ranges for aerated, interstitial, and consolidated lung tissue.
  • Semi-automatic segmentation for aerated and interstitial lung disease, with manual intervention required for consolidation analysis.

Main Results:

  • The quantitative analysis successfully segmented lung parenchyma, defining specific densitometry ranges for different pathological presentations.
  • A higher prevalence of "crazy paving" patterns was observed in patients admitted to intensive care, correlating with quantitative analysis findings.
  • The method demonstrated potential for objective evaluation, with semi-automatic segmentation being time-efficient for certain lung conditions.

Conclusions:

  • Threshold-based segmentation on 3D Slicer provides a viable open-source approach for quantitative analysis of lung parenchyma alterations in COVID-19 pneumonia.
  • This quantitative analysis can assist clinicians in understanding disease presentation and severity, thereby supporting informed patient management decisions.
  • The developed method offers a standardized and potentially more accessible tool for evaluating COVID-19 lung involvement compared to commercial alternatives.