scholarly journals Lung Segmentation and Iterative Weighted Averaging Smoothing Technique on Chest Ct Images

2019 ◽  
Vol 9 (1S3) ◽  
pp. 383-387
Keyword(s):  
Computed Tomography ◽  
Anisotropic Diffusion ◽  
Clustering Algorithm ◽  
Chest Ct ◽  
Weighted Averaging ◽  
Lung Tumour ◽  
Lung Segmentation ◽  
Averaging Technique ◽  
Diffusion Filtering ◽  
Nonlinear Anisotropic Diffusion

Computed Tomography (CT) is one of the most commonly used imaging modalities for tumour detection and diagnosis, due to its high spatial resolution, fast imaging speed and wide availability. Nodules of the lungs and pathological residues with varied diameter can be comfortably viewed by computed tomography and can be categorized as benign or malignant. The key intention of this segmentation and smoothing is to develop an efficient methodology for an automated lung tumour diagnosis. Segmentation is the quantitative preprocessing step used in the chest CT analysis. The iterative weighted averaging technique is used in addressing the issues related to the segmentation and smoothing method employed in this paper. The methodology incorporates the accurate Lung Segmentation, enclosure of Juxtapleural nodules, the proper insertion of the bronchial vessels for enhancing the smoothness of the segmented Lung area. The steps involved in this paper include Image preprocessing by nonlinear anisotropic diffusion filtering, Thorax Extraction, Lung segmentation and iterative weighted averaging technique for smoothing the contours. The main feature in choosing the nonlinear anisotropic diffusion filtering is for properly preserving the regions unaffected with cancer and also to differentiate the artefacts involved due to the subject movements. In the fuzzy c- means clustering algorithm, the lung parenchyma is identified from the thorax region based on the fuzzy membership value and the affected regions are attached. The standard execution time for segmentation process of one dataset is 1.91s, it is the more rapid process than the manual segmentation.

scholarly journals Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2025
Author(s):  
Jasjit S. Suri ◽  
Sushant Agarwal ◽  
Pranav Elavarthi ◽  
Rajesh Pathak ◽  
Vedmanvitha Ketireddy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Ground Truth ◽  
Chest Ct ◽  
Learning Models ◽  
Lung Segmentation ◽  
Variability Analysis ◽  
Experience Levels ◽  
Training Stage ◽  
Variability Study

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

scholarly journals Nonlinear anisotropic diffusion filtering of three-dimensional image data from two-photon microscopy

2004 ◽  
Vol 9 (6) ◽  
pp. 1253 ◽  
Author(s):  
Philip. J. Broser ◽  
R. Schulte ◽  
S. Lang ◽  
A. Roth Fritjof ◽  
Helmchen ◽  
...  
Keyword(s):  
Anisotropic Diffusion ◽  
Three Dimensional ◽  
Image Data ◽  
Dimensional Image ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Diffusion Filtering ◽  
Nonlinear Anisotropic Diffusion

scholarly journals Correlation between serum carbohydrate antigen 19-9 levels and computed tomography severity score in patients with nontuberculous mycobacterial pulmonary disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kangjoon Kim ◽  
Seung Hyun Yong ◽  
Su Hwan Lee ◽  
Sang Hoon Lee ◽  
Ah Young Leem ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Disease Activity ◽  
Pulmonary Disease ◽  
Severity Score ◽  
Partial Correlation ◽  
Elevated Serum ◽  
Carbohydrate Antigen ◽  
Chest Ct ◽  
Clinical Biomarker ◽  
Serum Carbohydrate Antigen

AbstractThere is no validated clinical biomarker for disease severity or treatment response for nontuberculous mycobacterial pulmonary disease (NTM-PD). We investigated the correlation between elevated serum carbohydrate antigen (CA) 19-9 levels and NTM-PD disease activity, defined using an imaging severity score based on chest computed tomography (CT). We retrospectively examined 79 patients with NTM-PD who underwent serum CA19-9 level assessments and chest CT less than 1 month apart. NTM-PD severity was rated using a CT-based scoring system. The correlation between the CT score and serum CA19-9 levels was evaluated. Chest CT revealed nodular bronchiectasis without cavitation in most patients (78.5%). Serum CA19-9 levels were elevated in 19 (24%) patients. Serum CA19-9 levels were positively correlated with the total CT score and bronchiectasis, bronchiolitis, cavity, and consolidation subscores. Partial correlation analysis revealed a significant positive correlation between serum CA19-9 levels and CT scores for total score and bronchiectasis, bronchiolitis, cavitation, and consolidation subscores after controlling for age, sex, and BMI. Serum CA19-9 levels were positively correlated with the CT severity score for NTM-PD. Serum CA19-9 may be useful in evaluating disease activity or therapeutic response in patients with NTM-PD.

scholarly journals Correlation between age, sex, and severity of Coronavirus disease-19 based on chest computed tomography severity scoring system

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Shimaa Farghaly ◽  
Marwa Makboul
Keyword(s):  
Computed Tomography ◽  
Scoring System ◽  
Fatal Outcome ◽  
Age Groups ◽  
Significant Risk ◽  
Chest Ct ◽  
Age Group ◽  
Severity Scoring ◽  
Chest Computed Tomography ◽  
Severity Of The Disease

Abstract Background Coronavirus disease 2019 (COVID-19) is the most recent global health emergency; early diagnosis of COVID-19 is very important for rapid clinical interventions and patient isolation; chest computed tomography (CT) plays an important role in screening, diagnosis, and evaluating the progress of the disease. According to the results of different studies, due to high severity of the disease, clinicians should be aware of the different potential risk factors associated with the fatal outcome, so chest CT severity scoring system was designed for semi-quantitative assessment of the severity of lung disease in COVID-19 patients, ranking the pulmonary involvement on 25 points severity scale according to extent of lung abnormalities; this study aims to evaluate retrospectively the relationship between age and severity of COVID-19 in both sexes based on chest CT severity scoring system. Results Age group C (40–49 year) was the commonest age group that was affected by COVID-19 by 21.3%, while the least affected group was group F (≥ 70 years) by only 6.4%. As regards COVID-RADS classification, COVID-RADS-3 was the most commonly presented at both sexes in all different age groups. Total CT severity lung score had a positive strong significant correlation with the age of the patient (r = 0.64, P < 0.001). Also, a positive strong significant correlation was observed between CT severity lung score and age in both males and females (r = 0.59, P < 0.001) and (r = 0.69, P < 0.001) respectively. Conclusion We concluded that age can be considered as a significant risk factor for the severity of COVID-19 in both sexes. Also, CT can be used as a significant diagnostic tool for the diagnosis of COVID-19 and evaluation of the progression and severity of the disease.

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