Objective

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The Coronavirus Disease 2019 (COVID-19) has become a global pandemic

causing millions of people to be infected worldwide. Imaging analysis based on computed tomography (CT) data is an important means of clinical diagnosis. A supercomputing-supported method is proposed for the construction of a new comprehensive CT analysis auxiliary system dealing with pneumonia.

Method

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The system consists of four parts: input processing module

preprocessing module

imaging analysis subsystem

and artificial intelligence(AI) analysis subsystem. Among the four parts

the imaging analysis subsystem detects the pneumonia features

distinguishes typical new coronary pneumonia by analyzing the typical imaging features

such as lung consolidation

ground-glass opacities

and crazy-paving pattern

and then comes to a conclusion of pneumonia. The AI analysis subsystem uses a deep learning model to classify typical viral pneumonia and COVID-19

which enhances the screening ability of pneumonia. Convolutional neural network is widely used as an effective algorithm for medical image analysis

particularly in image classification. It is also widely utilized in the CT image screening and has achieved good results

which has attracted the attention of domestic and foreign scholars and industry. The seasonable result derived from deep learning relies largely on the number and quality of training samples. Given the lack of training samples

the system selects transfer learning as the technical direction for model construction. Considering the quick response to the epidemic

the quality of easy maintenance and dynamic system updating is required. Thus

after comparing and analyzing the performance and classification effect indexes of many common image classification models

we build a transfer learning neural network model on the basis of inception. The entire neural network can be roughly divided into two parts: the first part uses a pre-trained inception network; the role of which is to convert image data into a one-dimensional feature vector. The second part uses a fully connected network to improve classification prediction. The imaging analysis method analyzes the image features of COVID-19

extracts the pneumonia feature areas

and carries out semantic analysis to achieve the delineation of the pneumonia target area. Simultaneously

the typical imaging characteristics of COVID-19 (such as ground glass shadow

infiltration shadow

and lung consolidation) are targeted. With regard to the pneumonia target area

a multi-level dynamic threshold segmentation is first used to determine the minimum lung tissue area (rectangular region of interest (ROI)). The extraction of the lung tissue area is designed as a normal workflow. For each ROI

pixel statistics

threshold segmentation

regional dissolution and expansion

and abnormal proofreading are used to obtain the pneumonia target area. Aiming at the relationship between the sizes of the pneumonia target area

a logical filter is established to detect the segmented distribution features and spatial relationship with the outer contour of the lung. Then

based on the characteristic relationship of typical new coronary pneumonia

the typical characteristics of new coronary pneumonia are outlined. The entire comprehensive analysis platform is built on the basis of the Tianhe artificial intelligence innovation-integrated platform. The Tianhe artificial intelligence innovation-integrated platform is based on the hardware fusion supporting the environment of Tianhe supercomputing

cloud computing

and big data

upon which realizes the existing mainstream deep learning framework. It is highly encapsulated with the processing model algorithm

forming a visual interactive template development environment covering multiple links such as data loading

model construction

training

verification

and solidified deployment. As a service on this supporting platform

CT image comprehensive analysis AI auxiliary system has access to the computing resources

data resources

and external service capabilities of the platform and finally achieves the rapid integration and dynamic update of the system during the pandemic.

Result

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After its release

the system has continuously and steadily provided new COVID-19 auxiliary diagnostic services and scientific research support for more than 30 hospitals and more than 100 scientific research institutions at home and abroad

providing important support for combating the epidemic.

Conclusion

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The supercomputing-supported new coronary pneumonia CT image comprehensive analysis auxiliary system construction method proposed in this paper has achieved important application on diagnosis and research. It is an effective way to achieve rapid deployment services and provide efficient support for emergencies. The system applies artificial intelligence technology using CT imaging to screen for COVID-19. By applying artificial intelligence to the screening of COVID-19 with pneumonia and giving reference opinions for auxiliary diagnosis

the marking and area statistics of the inflammatory regions are improved. The system achieves the combination of artificial intelligence traditional machine vision and deep learning technology to distinguish COVID-19 by using CT images. The combined route of viral pneumonitis feature extraction based on traditional machine vision and the COVID-19 image classification based on artificial intelligence technology has achieved a comprehensive analysis of medical image features and COVID-19 screening. The fast implementation mode of the fusion platform scenario is based on computing power and data support. Relying on the Tianhe artificial intelligence innovation-integrated service platform

the platform supports intelligent frontier innovation on the basis of computing power and data

implements an open model of simultaneous research and application

and has a multi-industry training resource model library and large-scale distributed training sources. With regard to rapid deployment and other service capabilities

this comprehensive analysis system is also the first public COVID-19 AI-assisted diagnostic system deployed online. Analysis based directly on digital imaging and communications in medicine(DICOM) data and video data will effectively improve the analysis efficiency

but it will involve data ethics and security-related issues; however

it is the developing direction that needs to be resolved in the future.