Abstract
BackgroundSpatial 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.MethodsA 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 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. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease.ResultsFor 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 subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations.ConclusionsBy 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 subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.
Highlights
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
Traction bronchiectasis often appears in the ground-glass opacities (GGO) area, and the formation of the subpleural band may cause structural distortion in some cases; as the disease progresses, multiple GGO and infiltration could appear in both lungs; in severe cases, pulmonary consolidation may occur, and pleural effusion is rare
Data collection 249 Computed tomography (CT) scans of 249 COVID-19 patients were collected from hospitals outside Shanghai, China for training the segmentation network, and 300 CT images were collected from the Public Health Center in Shanghai for validation
Summary
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. Other imaging signs include bronchiectasis and pleural thickening, but pleural effusion, pericardial effusion, enlargement of lymph nodes, pulmonary cavity, CT Halo sign and pneumothorax are rare These imaging features have been studied to distinguish the severity of the disease [5, 6] or to assess different stages of the disease during its time course [7,8,9]. For patients without severe respiratory distress during the disease course, abnormalities on chest CT reach to peak severity approximately 10 days after initial onset of the symptoms and gradually recover thereafter [9] These studies are mostly based on qualitative assessment of the images, and limited by the relatively small number of recruited patients. As the purpose of this paper is to analyzing the spatial and temporal distribution patterns of infection regions by automatically segmenting and aligning images, we demonstrated the most important features such as GGO, consolidation and HU distributions and their subsequent changes of the population studied
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