Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
Fei Shan,Yaozong Gao,Jun Wang,Weiya Shi,Nannan Shi,Miaofei Han,Zhong Xue,Dinggang Shen,Yuxin Shi +8 more
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TLDR
A deep learning (DL) based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung and possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings were discussed.Abstract:
CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported that bilateral and peripheral ground glass opacification (GGO) with or without consolidation are predominant CT findings in COVID-19 patients. However, due to lack of computerized quantification tools, only qualitative impression and rough description of infected areas are currently used in radiological reports. In this paper, a deep learning (DL)-based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung. The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients. For fast manual delineation of training samples and possible manual intervention of automatic results, a human-in-the-loop (HITL) strategy has been adopted to assist radiologists for infection region segmentation, which dramatically reduced the total segmentation time to 4 minutes after 3 iterations of model updating. The average Dice simiarility coefficient showed 91.6% agreement between automatic and manual infaction segmentations, and the mean estimation error of percentage of infection (POI) was 0.3% for the whole lung. Finally, possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings, were discussed.read more
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Journal ArticleDOI
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Laure Wynants,Laure Wynants,Ben Van Calster,Ben Van Calster,Gary S. Collins,Gary S. Collins,Richard D Riley,Georg Heinze,Ewoud Schuit,Marc J.M. Bonten,Darren Dahly,Johanna A A G Damen,Thomas P. A. Debray,Valentijn M.T. de Jong,Maarten De Vos,Paula Dhiman,Paula Dhiman,Maria C Haller,Michael O. Harhay,Liesbet Henckaerts,Pauline Heus,Michael Kammer,Nina Kreuzberger,Anna Lohmann,Kim Luijken,Jie Ma,Glen P. Martin,David J. McLernon,Constanza L Andaur Navarro,Johannes B. Reitsma,Jamie C. Sergeant,Chunhu Shi,Nicole Skoetz,Luc J.M. Smits,Kym I E Snell,Matthew Sperrin,René Spijker,René Spijker,Ewout W. Steyerberg,Toshihiko Takada,Ioanna Tzoulaki,Ioanna Tzoulaki,Sander M. J. van Kuijk,Bas C T van Bussel,Bas C T van Bussel,Iwan C. C. van der Horst,Florien S. van Royen,Jan Y Verbakel,Jan Y Verbakel,Christine Wallisch,Christine Wallisch,Jack Wilkinson,Robert Wolff,Lotty Hooft,Karel G.M. Moons,Maarten van Smeden +55 more
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
Journal ArticleDOI
Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks
TL;DR: Five pre-trained convolutional neural network-based models have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs and it has been seen that the pre- trained ResNet50 model provides the highest classification performance.
Journal ArticleDOI
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
Feng Shi,Jun Wang,Jun Shi,Ziyan Wu,Qian Wang,Zhenyu Tang,Kelei He,Yinghuan Shi,Dinggang Shen +8 more
TL;DR: This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.
Journal ArticleDOI
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.
TL;DR: In this paper, five pre-trained convolutional neural network-based models were proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs.
Journal ArticleDOI
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
TL;DR: Li et al. as discussed by the authors proposed a COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net) to automatically identify infected regions from chest CT slices, where a parallel partial decoder is used to aggregate the high-level features and generate a global map.
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