Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction

Shan, Fei; Gao, Yaozong; Wang, Jun; Shi, Wei; Shi, Nannan; Han, Meisheng; Zhang, Xue; Shen, Dinggang; Shi, Yuxin

doi:10.1002/mp.14609

Cited by 203 publications

(156 citation statements)

References 39 publications

(67 reference statements)

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“…(2020) proposed a similar method depending on the total severity score (TSS), which was reached by summing the five lobe scores. Shan et al. (2020) developed a deep learning based segmentation system, namely “VB-Net”, to automatically segment and quantify infection regions in CT scans of COVID-19 patients.…”

Section: Related Workmentioning

confidence: 99%

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

Zhao

Shi

et al. 2021

Medical Image Analysis

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Section: Related Workmentioning

confidence: 99%

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

Zhao

Shi

et al. 2021

Medical Image Analysis

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“…However, CT involves higher doses of radiation and requires patient handling, which could be complex ( 15 , 16 ). CT automatic analysis employs transfer learning, similarly to XR, the images are classified into three patterns (excluding viral pneumonia) ( 14 , 17 – 19 ). Additionally, with lung segmentation techniques, it is possible to separate affected regions from healthy ones and hence provide a quantification of pulmonary compromise ( 18 ).…”

Section: Application Of Ai In Medical Imagingmentioning

confidence: 99%

Would the Use of Artificial Intelligence in COVID-19 Patient Management Add Value to the Healthcare System?

Cossio

Gilardino

2021

Front. Med.

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“…Automatic segmentation of infection regions could be affected from the low contrast of the infection regions manifested as GGO in CT images and large variation of both shape and position across different patients. Our group developed a DL-based network called VB-Net, for lung CT image segmentation [10].…”

Section: Automatic Infection Segmentation Using Vb-netmentioning

confidence: 99%

Computing Infection Distributions and Longitudinal Evolution Patterns in Lung CT Images

Chen

shan

et al. 2020

Preprint

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Background: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. Methods: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on registering CT images within the lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. As a result, we compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across different course of the disease. Results: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground truth, the average Dice is 91.6%±10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1%±3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four distinct patterns (progression, absorption, enlargement, and further absorption) with remarkable concurrent HU patterns for GGO and consolidations.Conclusions: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four distinct disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

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Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction

Cited by 203 publications

References 39 publications

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

Would the Use of Artificial Intelligence in COVID-19 Patient Management Add Value to the Healthcare System?

Computing Infection Distributions and Longitudinal Evolution Patterns in Lung CT Images

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