Scientific Reports (Nov 2023)

Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity

  • Prachaya Khomduean,
  • Pongpat Phuaudomcharoen,
  • Totsaporn Boonchu,
  • Unchalisa Taetragool,
  • Kamonwan Chamchoy,
  • Nat Wimolsiri,
  • Tanadul Jarrusrojwuttikul,
  • Ammarut Chuajak,
  • Udomchai Techavipoo,
  • Numfon Tweeatsani

DOI
https://doi.org/10.1038/s41598-023-47743-z
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 11

Abstract

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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.