The quantification of COVID-19 infection in CT images using deep learning has not been investigated. Clinically there is no automatic tool to quantify the infection volume for COVID-19 patients. Purpose: To develop a deep learning (DL)-based system for automatic segmentation and quantification of infection regions as well as the entire lung from chest CT scans. Materials and methods: The DL-based segmentation employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The system is trained using 249 COVID-19 patients, and validated using 300 new COVID-19 patients. To accelerate the manual delineation of CT images for training, a human-in-the-loop (HITL) strategy is adopted to assist radiologists to refine automatic annotation of each case. To evaluate the performance of the DL-based system, Dice similarity coefficient, the differences of volume and percentage of infection (POI) are calculated between automatic and manual segmentation results on the validation set. Results: The proposed system yielded Dice similarity coefficients of 91.6%±10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases of fully manual delineation that often takes 1 to 5 hours, the proposed human-in-the-loop strategy can dramatically reduce the delineation time to 4 minutes after 3 iterations of model updating. Conclusion: A DL-based segmentation system was developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation showed high accuracy for automatic infection region delineation, POI metrics.
In REF , Shan et al. present a method to automatically segment and quantify infection in CT scans of COVID-19 patients.
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
ArXiv
ArXiv
Computer Science Engineering Biology