Segmentation of Lung Lobes and Lesions in Chest CT for the Classification of COVID-19 Severity (preprint)

ABSTRACT

To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consists of 100 COVID-19 patients acquired from Chulabhorn Hospital, divided into 25 cases without lung lesions and 75 cases with lung lesions categorized severity by radiologists regarding TSS. The model combines a 3D-UNet with pre-trained DenseNet and ResNet models for lung lobe segmentation and calculation of the percentage of lung involvement related to COVID-19 infection as well as TSS measured by the Dice similarity coefficient (DSC). Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with Dice similarity coefficients of 0.929 and 0.842, respectively. The calculated TSSs are similar to those evaluated by radiologists, with an R2 of 0.833. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.993 and 0.836, respectively.