COVID-19 Detection from Lung CT-Scans using a Fuzzy Integral-based CNN Ensemble

AbstractA novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

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Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2021.3117564 ◽

2021 ◽

pp. 1-1

Author(s):

Guotai Wang ◽

Shuwei Zhai ◽

Giovanni Lasio ◽

Baoshe Zhang ◽

Byong Yi ◽

...

Keyword(s):

Pulmonary Fibrosis ◽

Ct Scans ◽

Multi Scale ◽

Supervised Segmentation ◽

Radiation Induced ◽

Lung Ct

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Automated Detection of Small Pulmonary Nodules in Whole Lung CT Scans

Academic Radiology ◽

10.1016/j.acra.2007.01.029 ◽

2007 ◽

Vol 14 (5) ◽

pp. 579-593 ◽

Cited By ~ 19

Author(s):

Andinet A. Enquobahrie ◽

Anthony P. Reeves ◽

David F. Yankelevitz ◽

Claudia I. Henschke

Keyword(s):

Pulmonary Nodules ◽

Automated Detection ◽

Ct Scans ◽

Small Pulmonary Nodules ◽

Lung Ct

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Quantitative Computerized Two-Point Correlation Analysis of Lung CT Scans Correlates With Pulmonary Function in Pulmonary Sarcoidosis

CHEST Journal ◽

10.1378/chest.11-2027 ◽

2012 ◽

Vol 142 (6) ◽

pp. 1589-1597 ◽

Cited By ~ 7

Author(s):

Barbaros Selnur Erdal ◽

Elliott D. Crouser ◽

Vedat Yildiz ◽

Mark A. King ◽

Andrew T. Patterson ◽

...

Keyword(s):

Correlation Analysis ◽

Pulmonary Function ◽

Pulmonary Sarcoidosis ◽

Ct Scans ◽

Point Correlation ◽

Lung Ct

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Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans (Preprint)

Journal of Medical Internet Research ◽

10.2196/27468 ◽

2021 ◽

Author(s):

Mustafa Ghaderzadeh ◽

Farkhondeh Asadi ◽

Ramezan Jafari ◽

Davood Bashash ◽

Hassan Abolghasemi ◽

...

Keyword(s):

Ct Scans ◽

Cad System ◽

Deep Cnn ◽

Lung Ct

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DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning

Studies in Health Technology and Informatics - Public Health and Informatics ◽

10.3233/shti210175 ◽

2021 ◽

Author(s):

Amel Imene Hadj Bouzid ◽

Said Yahiaoui ◽

Anis Lounis ◽

Sid-Ahmed Berrani ◽

Hacène Belbachir ◽

...

Keyword(s):

Deep Learning ◽

Web Application ◽

Patient Management ◽

Image Interpretation ◽

Ct Scans ◽

Diagnostic Tools ◽

The World ◽

Aided Diagnosis ◽

Lung Ct ◽

Lung Ct Scan

Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

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Is pleural effusion in COVID-19 interstitial pneumonia related to in-hospital mortality?

Italian Journal of Medicine ◽

10.4081/itjm.2021.1440 ◽

2021 ◽

Vol 15 (1) ◽

Author(s):

Alberto Cereda ◽

Marco Toselli ◽

Anna Palmisano ◽

Riccardo Leone ◽

Davide Vignale ◽

...

Keyword(s):

Pleural Effusion ◽

Independent Predictor ◽

Northern Italy ◽

Ct Scans ◽

Respiratory Complications ◽

Prognostic Stratification ◽

Time Of Admission ◽

Lung Ct ◽

Epidemic Wave ◽

Pulmonary Ct

The recent severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) pandemic has highlighted the importance of pulmonary computed tomography (CT) for diagnosis and prognostic stratification of this new viral pneumonia. 1370 lung CT scans (performed at the time of admission) of consecutive patients hospitalized for SARS-CoV-2 in Northern Italy during the first epidemic wave were analyzed by a radiological CoreLab. The presence of pleural effusion on pulmonary CT scan was present in 188 patients (13.3% of the population) and identified a population with more comorbidities. Patients with pleural effusion had more cardio-respiratory complications with higher mortality. Pleural effusion was an independent predictor of death on multivariate analysis with an HR of 1.4 (95% confidence interval 1-1.9). Pulmonary CT pleural effusion was an independent predictor of mortality.

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