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Updated: May 29, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Method for minimizing observer variation for the quantitation of high-resolution computed tomographic signs of lung
Nicola Sverzellati1, Anand Devaraj, Sujal R Desai
1Department of Clinical Sciences, Section of Diagnostic Imaging, Padiglione Barbieri, University Hospital of Parma, Parma, Italy. nicolasve@tiscali.it
Objectives:
This study aimed to describe a method of reducing interobserver variation associated with the visual quantitation of high-resolution computed tomographic (HRCT) signs of airways and interstitial lung disease (ILD).
Methods:
The HRCT scans of 2 cohorts of patients with airways disease (n = 144) and ILD (n = 109) were evaluated by 2 observers. Selected signs of airways disease were evaluated: (1) bronchial wall thickness and (2) the extent of the decreased attenuation. In the ILD group, the total extent of disease was scored. These 3 HRCT signs were scored by 2 observers independently using a standard method. The observers rescored the CT scans with a new scoring system (continuous learning method, CLM).
Results:
Observer agreement for CT signs was superior for CLM: bronchial wall thickness κw increased from 0.51 to 0.76; for decreased attenuation, κw increased from 0.34 to 0.81; and for ILD extent, κw increased from 0.53 to 0.87.
Conclusions:
The CLM reduces noise from observer variation in studies that require visual quantitation of HRCT signs of lung disease.
