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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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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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Published on: December 19, 2020

Lung nodule segmentation in chest computed tomography using a novel background estimation method.

Pablo G Cavalcanti¹, Shahram Shirani¹, Jacob Scharcanski¹

¹1 Department of Informatics, Federal University of Technology-Paraná, Via do Conhecimento Km 1, Pato Branco, PR, Brazil ; 2 Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada ; 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil ; 4 Department of Radiology, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada ; 5 Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.

Quantitative Imaging in Medicine and Surgery

|March 17, 2016

Summary

This summary is machine-generated.

This study introduces a novel method for lung nodule segmentation, achieving over 99% accuracy. This technique aids physicians in differentiating cancerous lung nodules from benign ones.

Keywords:

Lung nodules background estimation chest CT segmentation

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Area of Science:

Medical imaging
Computer-aided diagnosis
Oncology

Background:

Lung cancer is a leading cause of cancer mortality globally.
Accurate lung nodule segmentation is crucial for distinguishing malignant from benign lesions.
Segmentation is challenging, particularly when nodules are attached to anatomical structures.

Purpose of the Study:

To propose a novel method for lung nodule segmentation.
To improve the accuracy of computer-aided diagnosis systems for lung cancer.
To address challenges in segmenting nodules connected to anatomical structures.

Main Methods:

A new method is proposed to estimate the background of lung nodule areas.
This background estimation is utilized to enhance the nodule segmentation process.
The approach focuses on improving segmentation when nodules are adjacent to other tissues.

Main Results:

The proposed method achieved over 99% accuracy in lung nodule segmentation.
A false positive rate (FPR) of less than 1% was recorded.
Experimental results demonstrate high precision in identifying lung nodules.

Conclusions:

The developed method significantly outperforms existing state-of-the-art approaches.
This technique shows strong potential for integration into medical image processing systems.
The findings support the use of this method for improved lung cancer diagnosis.