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Updated: Apr 2, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Automatic segmentation and analysis of the main pulmonary artery on standard post-contrast CT studies using iterative
Daniel Moses1,2, Claude Sammut3, Tatjana Zrimec3,4
1School of Computer Science and Engineering, University of New South Wales, Sydney, 2052, Australia. daniel.moses@unsw.edu.au.
Purpose:
To describe an algorithm for the accurate segmentation of the main pulmonary artery (MPA) and determining its length, mid-cross-sectional area and mid-circumferential perimeter. This will help with accurate, rapid and reproducible MPA measurements which can be used to detect diseases that cause raised pulmonary arterial pressure, and allow standardized serial measurements to assess progression or response to treatment.
Method:
We perform MPA segmentation using a novel approach based on erosion and dilation. A centerline is then determined by skeletonization, graph construction and spline fitting. MPA cross sections perpendicular to the centerline are analyzed in order to determine MPA length, and mid-cross-sectional area and perimeter. The technique was developed using four normal chest CT data sets and then tested on twenty normal post-contrast chest CT studies. Results are compared to manual segmentation and measurement by a thoracic radiologist.
Results:
The mean MPA length, mid-cross-sectional area and mid-circumferential perimeter of the twenty test data sets, calculated by our algorithm, are 43.6 [Formula: see text] 9.2 mm, 552.9 [Formula: see text] 132.4[Formula: see text] and [Formula: see text], respectively, compared with [Formula: see text] and [Formula: see text] obtained manually by the radiologist. Our technique shows high correlation with the manually determined parameters for both mid- cross-sectional area ([Formula: see text]) and length ([Formula: see text]), and good correlation for mid-circumferential perimeter ([Formula: see text]).
Conclusion:
Our algorithm is a robust accurate automated method for obtaining measurements of the MPA. This allows a more standardized method for determining length, and mid- cross-sectional area/perimeter and therefore allows more accurate comparison of MPA measurements.
Insights
An automated algorithm accurately segments the main pulmonary artery (MPA), providing reproducible measurements for disease detection and treatment monitoring. This novel method enhances diagnostic capabilities for pulmonary arterial pressure conditions.
Area of Science:
- Medical Imaging
- Cardiovascular Imaging
- Pulmonary Artery Analysis
Background:
- Accurate measurement of the main pulmonary artery (MPA) is crucial for diagnosing and monitoring diseases associated with elevated pulmonary arterial pressure.
- Current methods for MPA measurement can be time-consuming and lack reproducibility, hindering standardized clinical assessment.
Purpose of the Study:
- To develop and validate an automated algorithm for precise segmentation and measurement of the MPA.
- To determine MPA length, mid-cross-sectional area, and mid-circumferential perimeter accurately and rapidly.
- To facilitate standardized serial measurements for disease progression and treatment response assessment.
Main Methods:
- A novel segmentation approach using erosion and dilation techniques was employed for MPA identification.
- Centerline determination was achieved through skeletonization, graph construction, and spline fitting.
- MPA cross-sections perpendicular to the centerline were analyzed to derive morphometric parameters, validated against manual measurements by a radiologist.
Main Results:
- The algorithm demonstrated high correlation with manual measurements for MPA mid-cross-sectional area (r=[Formula: see text]) and length (r=[Formula: see text]).
- Good correlation was observed for the mid-circumferential perimeter (r=[Formula: see text]).
- Mean MPA measurements obtained by the algorithm were comparable to radiologist-derived values.
Conclusions:
- The developed algorithm offers a robust and accurate automated method for MPA morphometric analysis.
- This automated approach enables more standardized and reproducible MPA measurements.
- Improved accuracy and standardization in MPA measurements can enhance clinical decision-making for pulmonary vascular diseases.
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