1 research outputs found
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
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.Comment: 23 pages, 6 figure