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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.

Ali Afzali¹, Farshid Babapour Mofrad², Majid Pouladian³

¹Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Journal of Digital Imaging

|March 23, 2021

Summary

This summary is machine-generated.

This study introduces two lung shape models to analyze 2D lung shape variations in medical imaging. These models improve lung field segmentation accuracy using active shape models (ASM), achieving high Dice similarity coefficients.

Keywords:

Active shape model Fourier descriptor modelling Inter-patient 2D lung shape variations Model evaluation Principal component analysis Segmentation Statistical shape model

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Area of Science:

Medical Imaging
Anatomical Variation Analysis
Pulmonary Disease Diagnosis

Background:

Accurate lung shape analysis and understanding anatomical variations are crucial in medical imaging.
Normal lung shape variations can indicate healthy lungs, while abnormal variations may signal pulmonary diseases.
Inter-patient lung shape variability presents challenges in medical image analysis and segmentation.

Purpose of the Study:

To develop and compare two distinct lung shape models for analyzing inter-patient 2D lung shape variations.
To evaluate the impact of different reference points in registration on lung shape variation estimation.
To utilize these models within an active shape model (ASM) framework for accurate lung field segmentation.

Main Methods:

Development of two unique 2D lung shape models with differing reference points for registration.
Application of Principal Component Analysis (PCA) to model lung shape variations.
Implementation of an Active Shape Model (ASM) technique for lung field segmentation.
Validation using leave-one-out cross-validation.

Main Results:

The developed lung shape models, combined with PCA, effectively captured over 95% of total lung shape variations using only the first 7 principal component (PC) modes.
The ASM-based segmentation method achieved high accuracy, with average Dice Similarity Coefficients of 97.1% for the right lung and 96.1% for the left lung.
The proposed segmentation method demonstrated superior stability and accuracy compared to existing model-based techniques for inter-patient lung field segmentation.

Conclusions:

The proposed lung shape models provide a robust representation of inter-patient 2D lung shape variations.
The integration of these models with ASM offers a highly accurate and stable approach for lung field segmentation in medical imaging.
This methodology holds significant potential for improving the diagnosis and monitoring of pulmonary diseases through enhanced medical image analysis.