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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
Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images
M S Brown1, L S Wilson, B D Doust
1Department of Radiological Sciences, School of Medicine, University of California, Los Angeles, USA. mbrown@endeavour.radsci.ucla.edu
Summary
This study introduces an automated system for analyzing lung boundaries in chest X-rays. The knowledge-based approach accurately identifies lung edges and detects abnormalities, achieving high sensitivity and specificity.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Radiology
- Anatomical Modeling
Background:
- Accurate segmentation of lung boundaries in chest X-rays is crucial for diagnosing various pulmonary conditions.
- Current methods may be limited in precision and automation for detecting subtle abnormalities.
- Anatomical knowledge integration can potentially improve the robustness of image analysis systems.
Purpose of the Study:
- To develop and evaluate a knowledge-based system for automated segmentation and analysis of lung boundaries in chest X-rays.
- To identify and report abnormal features associated with the lung boundary.
- To assess the system's performance against expert radiologist assessments.
Main Methods:
- A knowledge-based approach was employed, integrating an anatomical model of lung boundaries.
- Parametric features were used to match image edges to the anatomical model.
- A modular system architecture comprising a model, image processing, inference engine, and blackboard was utilized.
- Automated identification of lung boundary edges and abnormal features was performed.
Main Results:
- The system successfully segmented lung boundaries and identified abnormal features.
- Preliminary testing on 14 chest X-ray images demonstrated high performance.
- The system achieved a sensitivity of 88% and a specificity of 95% for detecting 18 abnormalities.
- Performance was validated against assessments made by an experienced radiologist.
Conclusions:
- The developed knowledge-based system offers an effective automated solution for lung boundary segmentation and analysis in chest X-rays.
- The system shows significant potential for aiding radiologists in the detection of pulmonary abnormalities.
- Further validation on larger datasets is warranted to confirm its clinical utility.
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