Publications by authors named "V Y Chernina"

7 Publications

A phantom study to optimise the automatic tube current modulation for chest CT in COVID-19.

Eur Radiol Exp 2021 05 28;5(1):21. Epub 2021 May 28.

Radiation Protection of Patients Unit, International Atomic Energy Agency, Vienna, Austria.

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) pandemic. The expert organisations recommend more cautious use of thoracic computed tomography (CT), opting for low-dose protocols. We aimed at determining a threshold value of automatic tube current modulation noise index below which there is a chance to miss an onset of ground-glass opacities (GGO) in COVID-19. A team of radiologists and medical physicists performed 25 phantom CT studies using different automatic tube current modulation settings (Exposure3D technology). We then conducted a retrospective evaluation of the chest CT images from 22 patients with COVID-19 and calculated the density difference between the GGO and unaffected tissue. Finally, the results were matched to the phantom study results to determine the minimum noise index threshold value. The minimum density difference at the onset of COVID-19 was 252 HU (p < 0.001). This was found to correspond to the Exposure 3D noise index of 36. We established the noise index threshold of 36 for the Canon scanner without iterative reconstructions, allowing for a decrease in the dose-length product by 80%. The proposed protocol needs to be validated in a prospective study.
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http://dx.doi.org/10.1186/s41747-021-00218-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159722PMC
May 2021

A simplified cluster model and a tool adapted for collaborative labeling of lung cancer CT scans.

Comput Methods Programs Biomed 2021 Apr 18;206:106111. Epub 2021 Apr 18.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Petrovka str., 24, Moscow, 127051, Russia; Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Vavilova str., 44/2, Moscow, 119333, Russia. Electronic address:

Background And Objective: Lung cancer is the most common type of cancer with a high mortality rate. Early detection using medical imaging is critically important for the long-term survival of the patients. Computer-aided diagnosis (CAD) tools can potentially reduce the number of incorrect interpretations of medical image data by radiologists. Datasets with adequate sample size, annotation, and truth are the dominant factors in developing and training effective CAD algorithms. The objective of this study was to produce a practical approach and a tool for the creation of medical image datasets.

Methods: The proposed model uses the modified maximum transverse diameter approach to mark a putative lung nodule. The modification involves the possibility to use a set of overlapping spheres of appropriate size to approximate the shape of the nodule. The algorithm embedded in the model also groups the marks made by different readers for the same lesion. We used the data of 536 randomly selected patients of Moscow outpatient clinics to create a dataset of standard-dose chest computed tomography (CT) scans utilizing the double-reading approach with arbitration. Six volunteer radiologists independently produced a report for each scan using the proposed model with the main focus on the detection of lesions with sizes ranging from 3 to 30 mm. After this, an arbitrator reviewed their marks and annotations.

Results: The maximum transverse diameter approach outperformed the alternative methods (3D box, ellipsoid, and complete outline construction) in a study of 10,000 computer-generated tumor models of different shapes in terms of accuracy and speed of nodule shape approximation. The markup and annotation of the CTLungCa-500 dataset revealed 72 studies containing no lung nodules. The remaining 464 CT scans contained 3151 lesions marked by at least one radiologist: 56%, 14%, and 29% of the lesions were malignant, benign, and non-nodular, respectively. 2887 lesions have the target size of 3-30 mm. Only 70 nodules were uniformly identified by all the six readers. An increase in the number of independent readers providing CT scans interpretations led to an accuracy increase associated with a decrease in agreement. The dataset markup process took three working weeks.

Conclusions: The developed cluster model simplifies the collaborative and crowdsourced creation of image repositories and makes it time-efficient. Our proof-of-concept dataset provides a valuable source of annotated medical imaging data for training CAD algorithms aimed at early detection of lung nodules. The tool and the dataset are publicly available at https://github.com/Center-of-Diagnostics-and-Telemedicine/FAnTom.git and https://mosmed.ai/en/datasets/ct_lungcancer_500/, respectively.
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http://dx.doi.org/10.1016/j.cmpb.2021.106111DOI Listing
April 2021

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

Med Image Anal 2021 07 1;71:102054. Epub 2021 Apr 1.

Skolkovo Institute of Science and Technology, Moscow, Russia. Electronic address:

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset.
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http://dx.doi.org/10.1016/j.media.2021.102054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015379PMC
July 2021

Differences in Temporal Volume between Males and Females and the Influence of Age and BMI: A Cross-Sectional CT-Imaging Study.

Facial Plast Surg 2021 Mar 8. Epub 2021 Mar 8.

Department of Clinical Anatomy, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.

Background:  The temple has been identified as one of the most compelling facial regions in which to seek aesthetic improvement-both locally and in the entire face-when injecting soft tissue fillers.

Objective:  The objective of this study is to identify influences of age, gender, and body mass index (BMI) on temporal parameters to better understand clinical observations and to identify optimal treatment strategies for treating temporal hollowing.

Methods:  The sample consisted of 28 male and 30 female individuals with a median age of 53 (34) years and a median BMI of 27.00 (6.94) kg/m. The surface area of temporal skin, the surface area of temporal bones, and the temporal soft tissue volume were measured utilizing postprocessed computed tomography (CT) images via the Hausdorff minimal distance algorithm. Differences between the investigated participants related to age, BMI, and gender were calculated.

Results:  Median skin surface area was greater in males compared with females 5,100.5 (708) mm versus 4,208.5 (893) mm ( < 0.001) as was the median bone surface area 5,329 (690) mm versus 4,477 (888) mm ( < 0.001). Males had on average 11.04 mL greater temporal soft tissue volume compared with age and BMI-matched females with  < 0.001. Comparing the volume between premenopausal versus postmenopausal females, the median temporal soft tissue volume was 46.63 mL (11.94) versus 40.32 mL (5.69) ( = 0.014).

Conclusion:  The results of this cross-sectional CT imaging study confirmed previous clinical and anatomical observations and added numerical evidence to those observations for a better clinical integration of the data.
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http://dx.doi.org/10.1055/s-0041-1725201DOI Listing
March 2021

[Epicardial fat Tissue Volumetry: Comparison of Semi-Automatic Measurement and the Machine Learning Algorithm].

Kardiologiia 2020 Oct 14;60(9):46-54. Epub 2020 Oct 14.

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow.

Aim        To compare assessments of epicardial adipose tissue (EAT) volumes obtained with a semi-automatic, physician-performed analysis and an automatic analysis using a machine-learning algorithm by data of low-dose (LDCT) and standard computed tomography (CT) of chest organs.Material and methods        This analytical, retrospective, transversal study randomly included 100 patients from a database of a united radiological informational service (URIS). The patients underwent LDCT as a part of the project "Low-dose chest computed tomography as a screening method for detection of lung cancer and other diseases of chest organs" (n=50) and chest CT according to a standard protocol (n=50) in outpatient clinics of Moscow. Each image was read by two radiologists on a Syngo. via VB20 workstation. In addition, each image was evaluated with a developed machine-learning algorithm, which provides a completely automatic measurement of EAT.Results   Comparison of EAT volumes obtained with chest LDCT and CT showed highly consistent results both for the expert-performed semi-automatic analyses (correlation coefficient >98 %) and between the expert layout and the machine-learning algorithm (correlation coefficient >95 %). Time of performing segmentation and volumetry on one image with the machine-learning algorithm was not longer than 40 sec, which was 30 times faster than the quantitative analysis performed by an expert and potentially facilitated quantification of the EAT volume in the clinical conditions.Conclusion            The proposed method of automatic volumetry will expedite the analysis of EAT for predicting the risk of ischemic heart disease.
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http://dx.doi.org/10.18087/cardio.2020.9.n1111DOI Listing
October 2020