scholarly journals From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

2020 ◽  
Vol 30 (12) ◽  
pp. 6828-6837 ◽  
Author(s):  
Zhang Li ◽  
Zheng Zhong ◽  
Yang Li ◽  
Tianyu Zhang ◽  
Liangxin Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Quantitative Analysis ◽  
Community Acquired Pneumonia ◽  
Ct Scans ◽  
Thick Section

scholarly journals Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5878 ◽  
Author(s):  
Fares Bougourzi ◽  
Riccardo Contino ◽  
Cosimo Distante ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Deep Learning ◽  
Community Acquired Pneumonia ◽  
Research Topic ◽  
The Other ◽  
Ct Scans ◽  
Second Stage ◽  
Volumetric Ct ◽  
Active Research ◽  
Validation Set ◽  
Learning Architectures

Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

scholarly journals From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans

2020 ◽  
Author(s):  
Zhang Li ◽  
Zheng Zhong ◽  
Yang Li ◽  
Tianyu Zhang ◽  
Liangxin Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Disease Severity ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Ct Images ◽  
Chest Ct ◽  
Ct Scans ◽  
Thick Section ◽  
Dice Coefficient ◽  
Lung Abnormalities

AbstractBackgroundThick-section CT scanners are more affordable for the developing countries. Considering the widely spread COVID-19, it is of great benefit to develop an automated and accurate system for quantification of COVID-19 associated lung abnormalities using thick-section chest CT images.PurposeTo develop a fully automated AI system to quantitatively assess the disease severity and disease progression using thick-section chest CT images.Materials and MethodsIn this retrospective study, a deep learning based system was developed to automatically segment and quantify the COVID-19 infected lung regions on thick-section chest CT images. 531 thick-section CT scans from 204 patients diagnosed with COVID-19 were collected from one appointed COVID-19 hospital from 23 January 2020 to 12 February 2020. The lung abnormalities were first segmented by a deep learning model. To assess the disease severity (non-severe or severe) and the progression, two imaging bio-markers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU). The performance of lung abnormality segmentation was examined using Dice coefficient, while the assessment of disease severity and the disease progression were evaluated using the area under the receiver operating characteristic curve (AUC) and the Cohen’s kappa statistic, respectively.ResultsDice coefficient between the segmentation of the AI system and the manual delineations of two experienced radiologists for the COVID-19 infected lung abnormalities were 0.74±0.28 and 0.76±0.29, respectively, which were close to the inter-observer agreement, i.e., 0.79±0.25. The computed two imaging bio-markers can distinguish between the severe and non-severe stages with an AUC of 0.9680 (p-value< 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists were achieved on evaluating the changes of infection volumes.ConclusionsA deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 associated lung abnormalities, assess the disease severity and its progressions.Key ResultsA deep learning based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74).The computed imaging bio-markers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.968).The infection volume changes computed by the AI system was able to assess the COVID-19 progression (Cohen’s kappa 0.8220).Summary StatementA deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 infected lung regions, assess patients disease severity and their disease progressions.

PO-1718: Deep learning based auto-contouring of planning CT scans for rectal cancer

2020 ◽  
Vol 152 ◽  
pp. S949
Author(s):  
L. Bokhorst ◽  
M.H.F. Savenije ◽  
M.P.W. Intven ◽  
C.A.T. Van den Berg
Keyword(s):  
Rectal Cancer ◽  
Deep Learning ◽  
Ct Scans ◽  
Planning Ct

scholarly journals A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoguo Zhang ◽  
Dawei Wang ◽  
Jiang Shao ◽  
Song Tian ◽  
Weixiong Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Machine Learning Algorithms ◽  
Multi Layer Perceptron ◽  
Selection Methods ◽  
Medical Centers ◽  
Global Pandemic ◽  
Diagnostic Time ◽  
Logistic Regression Classifier

AbstractSince its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856–0.988) and 0.959 (95% CI 0.910–1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

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

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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