Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set

TL;DR

This study develops deep learning models to accurately segment infected lung regions in CT images of COVID-19 patients, enabling estimation of infection proportions to assist clinical severity assessment and prognosis prediction.

Contribution

It introduces a novel pipeline combining deep learning segmentation with radiologist refinement to quantify infected lung regions in COVID-19 CT scans.

Findings

Achieved high Dice similarity coefficients of 0.972 for lung masks and 0.757 for infected regions.

Final infection proportion estimation showed 0.961 Pearson's correlation and 11.7% mean absolute percent error.

The method provides visual and quantitative tools for clinical severity assessment and prognosis of COVID-19.

Abstract

Utilizing computed tomography (CT) images to quickly estimate the severity of cases with COVID-19 is one of the most straightforward and efficacious methods. Two tasks were studied in this present paper. One was to segment the mask of intact lung in case of pneumonia. Another was to generate the masks of regions infected by COVID-19. The masks of these two parts of images then were converted to corresponding volumes to calculate the physical proportion of infected region of lung. A total of 129 CT image set were herein collected and studied. The intrinsic Hounsfiled value of CT images was firstly utilized to generate the initial dirty version of labeled masks both for intact lung and infected regions. Then, the samples were carefully adjusted and improved by two professional radiologists to generate the final training set and test benchmark. Two deep learning models were evaluated: UNet and 2.5D UNet. For the segment of infected regions, a deep learning based classifier was followed to remove unrelated blur-edged regions that were wrongly segmented out such as air tube and blood vessel tissue etc. For the segmented masks of intact lung and infected regions, the best method could achieve 0.972 and 0.757 measure in mean Dice similarity coefficient on our test benchmark. As the overall proportion of infected region of lung, the final result showed 0.961 (Pearson's correlation coefficient) and 11.7% (mean absolute percent error). The instant proportion of infected regions of lung could be used as a visual evidence to assist clinical physician to determine the severity of the case. Furthermore, a quantified report of infected regions can help predict the prognosis for COVID-19 cases which were scanned periodically within the treatment cycle.