Computing infection distributions and longitudinal evolution patterns in lung CT images
Abstract 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...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-03-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-021-00588-2 |
| _version_ | 1830299273680388096 |
|---|---|
| author | Dongdong Gu Liyun Chen Fei Shan Liming Xia Jun Liu Zhanhao Mo Fuhua Yan Bin Song Yaozong Gao Xiaohuan Cao Yanbo Chen Ying Shao Miaofei Han Bin Wang Guocai Liu Qian Wang Feng Shi Dinggang Shen Zhong Xue |
| author_facet | Dongdong Gu Liyun Chen Fei Shan Liming Xia Jun Liu Zhanhao Mo Fuhua Yan Bin Song Yaozong Gao Xiaohuan Cao Yanbo Chen Ying Shao Miaofei Han Bin Wang Guocai Liu Qian Wang Feng Shi Dinggang Shen Zhong Xue |
| author_sort | Dongdong Gu |
| collection | DOAJ |
| description | Abstract 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 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. 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 subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, 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 subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations. |
| first_indexed | 2024-12-19T08:06:17Z |
| format | Article |
| id | doaj.art-1090d141d2674540a66355866edc80e6 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-12-19T08:06:17Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-1090d141d2674540a66355866edc80e62022-12-21T20:29:44ZengBMCBMC Medical Imaging1471-23422021-03-0121111210.1186/s12880-021-00588-2Computing infection distributions and longitudinal evolution patterns in lung CT imagesDongdong Gu0Liyun Chen1Fei Shan2Liming Xia3Jun Liu4Zhanhao Mo5Fuhua Yan6Bin Song7Yaozong Gao8Xiaohuan Cao9Yanbo Chen10Ying Shao11Miaofei Han12Bin Wang13Guocai Liu14Qian Wang15Feng Shi16Dinggang Shen17Zhong Xue18Hunan UniversityShanghai Jiao Tong UniversityShanghai Public Health Clinical CenterTongji HospitalSecond Xiangya Hospital of Central South UniversityChina-Japan Union Hospital of Jilin UniversityRuijin HospitalWest China Hospital of Sichuan UniversityShanghai United Imaging Intelligence Co., LtdShanghai United Imaging Intelligence Co., LtdShanghai United Imaging Intelligence Co., LtdShanghai United Imaging Intelligence Co., LtdShanghai United Imaging Intelligence Co., LtdShanghai United Imaging Intelligence Co., LtdHunan UniversityShanghai Jiao Tong UniversityShanghai United Imaging Intelligence Co., LtdShanghai United Imaging Intelligence Co., LtdShanghai United Imaging Intelligence Co., LtdAbstract 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 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. 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 subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, 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 subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.https://doi.org/10.1186/s12880-021-00588-2COVID-19SegmentationRegistrationCoronavirus infectionsLungProbability |
| spellingShingle | Dongdong Gu Liyun Chen Fei Shan Liming Xia Jun Liu Zhanhao Mo Fuhua Yan Bin Song Yaozong Gao Xiaohuan Cao Yanbo Chen Ying Shao Miaofei Han Bin Wang Guocai Liu Qian Wang Feng Shi Dinggang Shen Zhong Xue Computing infection distributions and longitudinal evolution patterns in lung CT images BMC Medical Imaging COVID-19 Segmentation Registration Coronavirus infections Lung Probability |
| title | Computing infection distributions and longitudinal evolution patterns in lung CT images |
| title_full | Computing infection distributions and longitudinal evolution patterns in lung CT images |
| title_fullStr | Computing infection distributions and longitudinal evolution patterns in lung CT images |
| title_full_unstemmed | Computing infection distributions and longitudinal evolution patterns in lung CT images |
| title_short | Computing infection distributions and longitudinal evolution patterns in lung CT images |
| title_sort | computing infection distributions and longitudinal evolution patterns in lung ct images |
| topic | COVID-19 Segmentation Registration Coronavirus infections Lung Probability |
| url | https://doi.org/10.1186/s12880-021-00588-2 |
| work_keys_str_mv | AT dongdonggu computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT liyunchen computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT feishan computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT limingxia computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT junliu computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT zhanhaomo computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT fuhuayan computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT binsong computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT yaozonggao computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT xiaohuancao computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT yanbochen computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT yingshao computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT miaofeihan computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT binwang computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT guocailiu computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT qianwang computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT fengshi computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT dinggangshen computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages AT zhongxue computinginfectiondistributionsandlongitudinalevolutionpatternsinlungctimages |