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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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CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.
R Karthik1, R Menaka1, M Hariharan2
1Centre for Cyber Physical Systems & School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Applied Soft Computing
|April 4, 2022
Summary
This study introduces an AI model using attention mechanisms on chest CT scans to assess COVID-19 severity. The novel framework accurately predicts patient risk, aiding in timely and appropriate treatment decisions.
Area of Science:
- Radiology
- Artificial Intelligence
- Medical Imaging
Background:
- Accurate patient criticality evaluation is crucial for effective COVID-19 treatment.
- Artificial Intelligence (AI) models can automate risk-stratification using clinical data.
- Chest CT findings like ground-glass opacities and consolidations correlate with disease severity.
Purpose of the Study:
- To develop a novel attention framework for estimating COVID-19 severity using weakly annotated chest CT scans.
- To create an AI model that provides a regression score for patient risk stratification.
Main Methods:
- A non-locality attention approach correlating features across 3D scan parts and scales.
- An explicit guidance mechanism using limited infection labeling for attention refinement.
- Cross-channel attention and global contextual awareness infusion into voxel features.
Main Results:
- The proposed attention framework achieved an R-squared score of 0.84.
- The model demonstrated a mean absolute difference of 0.133 on the MosMed dataset.
- The architecture effectively localized infection regions and refined features.
Conclusions:
- The novel attention framework shows significant potential for augmenting COVID-19 severity assessment.
- AI-driven analysis of chest CT scans can improve patient risk stratification.
- The model precisely identifies and quantifies infection indicators for better prognostic studies.
