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Updated: Mar 24, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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.
Background:
Lung cancer results in the highest number of cancer deaths worldwide. The segmentation of lung nodules is an important task in computer systems to help physicians differentiate malignant lesions from benign lesions. However, it has already been observed that this may be a difficult task, especially when nodules are connected to an anatomical structure.
Methods:
This paper proposes a method to estimate the background of the nodule area and how this estimation is used to facilitate the segmentation task.
Results:
Our experiments indicate more than 99% of accuracy with less than 1% of false positive rate (FPR).
Conclusions:
The proposed methods achieved better results than a state-of-the-art approach, indicating potential to be used in medical image processing systems.
