scholarly journals Ct-Caps: Feature Extraction-Based Automated Framework for Covid-19 Disease Identification From Chest Ct Scans Using Capsule Networks

2021 ◽  
Author(s):  
Shahin Heidarian ◽  
Parnian Afshar ◽  
Arash Mohammadi ◽  
Moezedin Javad Rafiee MD ◽  
Anastasia Oikonomou MD ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Chest Ct ◽  
Ct Scans ◽  
Disease Identification

scholarly journals Cross-sectional analysis of follow-up chest MRI and chest CT scans in patients previously affected by COVID-19

2021 ◽  
Author(s):  
Martina Pecoraro ◽  
Stefano Cipollari ◽  
Livia Marchitelli ◽  
Emanuele Messina ◽  
Maurizio Del Monte ◽  
...  
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
Imaging Biomarker ◽  
Cross Sectional ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Sectional Analysis ◽  
Ct And Mri ◽  
Interlobular Septal Thickening

Abstract Purpose The aim of the study was to prospectively evaluate the agreement between chest magnetic resonance imaging (MRI) and computed tomography (CT) and to assess the diagnostic performance of chest MRI relative to that of CT during the follow-up of patients recovered from coronavirus disease 2019. Materials and methods Fifty-two patients underwent both follow-up chest CT and MRI scans, evaluated for ground-glass opacities (GGOs), consolidation, interlobular septal thickening, fibrosis, pleural indentation, vessel enlargement, bronchiolar ectasia, and changes compared to prior CT scans. DWI/ADC was evaluated for signal abnormalities suspicious for inflammation. Agreement between CT and MRI was assessed with Cohen’s k and weighted k. Measures of diagnostic accuracy of MRI were calculated. Results The agreement between CT and MRI was almost perfect for consolidation (k = 1.00) and change from prior CT (k = 0.857); substantial for predominant pattern (k = 0.764) and interlobular septal thickening (k = 0.734); and poor for GGOs (k = 0.339), fibrosis (k = 0.224), pleural indentation (k = 0.231), and vessel enlargement (k = 0.339). Meanwhile, the sensitivity of MRI was high for GGOs (1.00), interlobular septal thickening (1.00), and consolidation (1.00) but poor for fibrotic changes (0.18), pleural indentation (0.23), and vessel enlargement (0.50) and the specificity was overall high. DWI was positive in 46.0% of cases. Conclusions The agreement between MRI and CT was overall good. MRI was very sensitive for GGOs, consolidation and interlobular septal thickening and overall specific for most findings. DWI could be a reputable imaging biomarker of inflammatory activity.

scholarly journals CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans

2021 ◽  
pp. 1-1
Author(s):  
Tanvir Mahmud ◽  
Md Awsafur Rahman ◽  
Shaikh Anowarul Anowarul Fattah ◽  
Sun-Yuan Kung
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
Lesion Segmentation ◽  
Decoder Architecture

scholarly journals Hierarchical Prediction of Registration Misalignment using a Convolutional LSTM: Application to Chest CT Scans

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hessam Sokooti ◽  
Sahar Yousefi ◽  
Mohamed S. Elmahdy ◽  
Boudewijn P.F. Lelieveldt ◽  
Marius Staring
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
Convolutional Lstm

scholarly journals A Deep Learning Approach For Improved Segmentation Of Lesions Related To Covid-19 Chest CT Scans

2021 ◽  
Author(s):  
Vlad Vasilescu ◽  
Ana Neacsu ◽  
Emilie Chouzenoux ◽  
Jean-Christophe Pesquet ◽  
Corneliu Burileanu
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans ◽  
Learning Approach

Radiation Exposure in Standard and High-Resolution Chest CT Scans

CHEST Journal ◽  
1995 ◽  
Vol 107 (1) ◽  
pp. 113-115 ◽  
Author(s):  
Brigitte A.H.A. Van der Bruggen-Bogaarts ◽  
Johan J. Broerse ◽  
Jan-Willem J. Lammers ◽  
Paul F.G.M. Van Waes ◽  
Jacob Geleijns
Keyword(s):  
High Resolution ◽  
Radiation Exposure ◽  
Chest Ct ◽  
Ct Scans

scholarly journals COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing

2021 ◽  
pp. 100709
Author(s):  
Md. Kamrul Hasan ◽  
Md. Tasnim Jawad ◽  
Kazi Nasim Imtiaz Hasan ◽  
Sajal Basak Partha ◽  
Md. Masum Al Masba ◽  
...  
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
3D Cnn

Using chest CT scan and unsupervised machine learning for predicting and evaluating response to lumacaftor-ivacaftor in people with cystic fibrosis

2021 ◽  
pp. 2101344
Author(s):  
Alienor Campredon ◽  
Enzo Battistella ◽  
Clémence Martin ◽  
Isabelle Durieu ◽  
Laurent Mely ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Ct Scan ◽  
Structural Changes ◽  
Clinical Status ◽  
Chest Ct ◽  
Ct Scans ◽  
Wall Thickening ◽  
Unsupervised Machine Learning ◽  
One Year

ObjectivesLumacaftor-ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator known to improve clinical status in people with cystic fibrosis (CF). This study aimed to assess lung structural changes after one year of lumacaftor-ivacaftor treatment, and to use unsupervised machine learning to identify morphological phenotypes of lung disease that are associated with response to lumacaftor-ivacaftor.MethodsAdolescents and adults with CF from the French multicenter real-world prospective observational study evaluating the first year of treatment with lumacaftor-ivacaftor were included if they had pretherapeutic and follow-up chest computed tomography (CT)-scans available. CT scans were visually scored using a modified Bhalla score. A k-mean clustering method was performed based on 120 radiomics features extracted from unenhanced pretherapeutic chest CT scans.ResultsA total of 283 patients were included. The Bhalla score significantly decreased after 1 year of lumacaftor-ivacaftor (−1.40±1.53 points compared with pretherapeutic CT; p<0.001). This finding was related to a significant decrease in mucus plugging (−0.35±0.62 points; p<0.001), bronchial wall thickening (−0.24±0.52 points; p<0.001) and parenchymal consolidations (−0.23±0.51 points; p<0.001). Cluster analysis identified 3 morphological clusters. Patients from cluster C were more likely to experience an increase in percent predicted forced expiratory volume in 1 sec (ppFEV1) ≥5 under lumacaftor–ivacaftor than those in the other clusters (54% of responders versus 32% and 33%; p=0.01).ConclusionOne year treatment with lumacaftor-ivacaftor was associated with a significant visual improvement of bronchial disease on chest CT. Radiomics features on pretherapeutic CT scan may help in predicting lung function response under lumacaftor-ivacaftor.

scholarly journals The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110162
Author(s):  
Fengxia Zeng ◽  
Yong Cai ◽  
Yi Guo ◽  
Weiguo Chen ◽  
Min Lin ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Diagnostic Value ◽  
Rapid Identification ◽  
Chest Ct ◽  
Ct Scans ◽  
Ground Glass ◽  
X Ray ◽  
Chest X Ray ◽  
Laboratory Examinations ◽  
Sensitivity Specificity

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients ( p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs ( p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

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