Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity
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Author list: Khomduean, Prachaya; Phuaudomcharoen, Pongpat; Boonchu, Totsaporn; Taetragool, Unchalisa; Chamchoy, Kamonwan; Wimolsiri, Nat; Jarrusrojwuttikul, Tanadul; Chuajak, Ammarut; Techavipoo, Udomchai; Tweeatsani, Numfon
Publication year: 2023
Volume number: 13
Issue number: 1
ISSN: 20452322
Languages: English-Great Britain (EN-GB)
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 consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively. ฉ 2023, The Author(s).
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