MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset

Morozov, S. P.; Andreychenko, Anna; Pavlov, Nikolai; Vladzymyrskyy, A.V.; Ледихова, Н. В.; Gombolevskiy, Victor A.; Blokhin, Ivan; Gelezhe, Pavel; Гончарь, Алексей Владимирович; Chernina, Valeria

doi:10.1101/2020.05.20.20100362

Cited by 181 publications

(134 citation statements)

References 7 publications

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“…CAP subjects included in our database were all nonviral CAP. Data were collected in three different centers in Wuhan, and from four publicly available databases, LIDC-IDRI 31 , Tianchi-Alibaba 32 , MosMedData 33 , and CC-CCII 24 (described in Table 1 and "Methods").…”

Section: Resultsmentioning

confidence: 99%

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

et al. 2020

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Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

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Section: Resultsmentioning

confidence: 99%

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

et al. 2020

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“… 17 18 For the lesion segmentation task, 6,971 manually segmented slices (train: 5,854, test: 1,117) from three publicly available datasets were used (20 cases, MosMed, MSD). 18 19 20 Then, for the data from Yeungnam University Medical Center, lesions were extracted using the trained models. The patches smaller than 13 mm were not used to avoid misclassification caused by wrong segmentation or noise such as motion artifacts, as this may have a negative effect on subsequent analysis.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective

et al. 2021

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Background It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. Methods This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The Lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. Results The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. Conclusion Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

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“…3) NSCLC Pleural Effusion (PE) segmentation: The CT scans in this dataset are the same as those in NSCLC left and right lung segmentation dataset, while pleural effusion is delineated for 78 cases [29], [30], [31]. 4) MosMed Dataset: This dataset contains 50 annotated COVID-19 CT scans that are provided by municipal hospitals in Moscow, Russia [32]. To evaluate the generalization ability of deep learning models, we used this dataset as an independent testing set in following benchmark settings.…”

Section: Accepted Articlementioning

confidence: 99%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

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Purpose Accurate segmentation of lung and infection in COVID‐19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID‐19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods To promote the development of data‐efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID‐19 cases, which contain current active research areas, for example, few‐shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results Based on the state‐of‐the‐art network, we provide more than 40 pretrained baseline models, which not only serve as out‐of‐the‐box segmentation tools but also save computational time for researchers who are interested in COVID‐19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions To the best of our knowledge, this work presents the first data‐efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID‐19 CT segmentation with limited data.

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MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset

Cited by 181 publications

References 7 publications

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

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