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

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

Published on: December 19, 2020

An automatic method for ground glass opacity nodule detection and segmentation from CT studies.

Jinghao Zhou¹, Sukmoon Chang, Dimitris N Metaxas

¹Dept. of Comput. Sci., Rutgers Univ., New Brunswick, NJ, USA. jhzhou@eden.rutgers.edu

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This study introduces an automated method for detecting and segmenting ground glass opacities (GGO) in CT scans. The novel approach accurately identifies GGO nodules, improving early lung cancer diagnosis.

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Area of Science:

Medical Imaging
Radiology
Computer-Aided Diagnosis

Background:

Ground glass opacity (GGO) on CT scans can indicate early lung cancer, but manual detection is challenging due to indistinct boundaries and observer variability.
Accurate and consistent identification of GGO is crucial for timely lung cancer treatment and improved patient prognosis.
Existing methods for GGO detection and segmentation lack reproducibility and efficiency.

Purpose of the Study:

To develop and validate a novel, automated method for the detection and segmentation of ground glass opacities (GGO) in chest CT images.
To enhance the accuracy and consistency of GGO identification, overcoming limitations of manual analysis.
To provide a robust tool for early lung cancer diagnosis through improved GGO characterization.

Main Methods:

A boosted k-nearest neighbor (k-NN) classifier utilizing Euclidean distance between nonparametric density estimates for GGO detection.
Automatic segmentation of detected GGO regions through analysis of a 3D texture likelihood map.
Application and validation of the method on clinical chest CT volumes containing GGO nodules.

Main Results:

The proposed method successfully detected all 10 GGO nodules in the clinical dataset.
A low false positive rate of only one nodule was observed.
Statistical validation demonstrated the classifier's effectiveness for automatic GGO detection and promising results for segmentation.

Conclusions:

The developed automated method offers a powerful and reproducible tool for GGO detection and segmentation in chest CT scans.
This approach has the potential to significantly aid in the early diagnosis and management of lung cancer.
The findings suggest a substantial improvement over manual GGO analysis, enhancing diagnostic accuracy and efficiency.