Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

TL;DR

This paper presents a deep learning system for automatic quantification of COVID-19 infection regions in lung CT scans, significantly aiding diagnosis and disease monitoring with high accuracy and efficiency.

Contribution

A novel deep learning-based segmentation system with human-in-the-loop strategy for rapid and accurate quantification of COVID-19 lung infections in CT images.

Findings

Achieved 91.6% Dice similarity coefficient between automatic and manual segmentations.

Reduced segmentation time to 4 minutes with iterative model updates.

Mean infection percentage estimation error was only 0.3%.

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.