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A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities.

Azrin Khan1,2, Rachael Garner1, Marianna La Rocca1,3

  • 1Laboratory of Neuro Imaging, Keck School of Medicine of USC, USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA USA.

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Summary

A new semi-automatic method accurately segments COVID-19 lung infections on CT scans. This approach aids in assessing disease severity and progression, improving patient care and reducing radiologist workload.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pulmonology

Background:

  • Accurate COVID-19 diagnosis and classification are critical for patient care and controlling viral spread.
  • Manual lung infection segmentation on CT scans is time-consuming and requires expert radiologic knowledge.
  • Existing deep learning methods for segmentation are limited by the lack of large, annotated datasets.

Purpose of the Study:

  • To develop a semi-automatic, threshold-based segmentation method for COVID-19 lung infections on CT scans.
  • To enable accurate calculation of Percentage of Lung Abnormality (PLA) for severity assessment and disease progression analysis.
  • To provide an efficient alternative to manual segmentation and improve upon existing automated methods.

Main Methods:

  • A semi-automatic, threshold-based segmentation technique was developed to identify regions of interest (ROIs) of lung infections in CT scans.
  • Infection masks were generated to calculate the Percentage of Lung Abnormality (PLA).
  • The method's performance was evaluated against ground truth and other segmentation techniques.

Main Results:

  • The proposed method demonstrated improved precision and specificity compared to other COVID-19 ROI segmentation methods.
  • Generated PLAs showed a small difference () from ground-truth values.
  • The semi-automatic segmentation achieved high accuracy in identifying infection ROIs.

Conclusions:

  • The developed semi-automatic segmentation method effectively identifies COVID-19 lung infections on CT scans.
  • This technique aids in accurate severity assessment and disease progression analysis.
  • The method shows potential to assist radiologists in managing COVID-19 patients.