High-quality chest CT segmentation to assess the impact of COVID-19 disease

Abstract

Purpose COVID-19 has spread rapidly worldwide since its initial appearance, creating the need for faster diagnostic methods and tools. Due to the high rate of false-negative RT-PCR tests, the role of chest CT examination has been investigated as an auxiliary procedure. The main goal of this work is to establish a well-defined strategy for 3D segmentation of the airways and lungs of COVID-19 positive patients from CT scans, including detected abnormalities. Their identification and the volumetric quantification could allow an easier classification in terms of gravity, extent and progression of the infection. Moreover, these 3D reconstructions can provide a high-impact tool to enhance awareness of the severity of COVID-19 pneumonia. Methods Segmentation process was performed utilizing a proprietary software, starting from six different stacks of chest CT images of subjects with and without COVID-19. In this context, a comparison between manual and automatic segmentation methods of the respiratory system was conducted, to assess the potential value of both techniques, in terms of time consumption, required anatomical knowledge and branch detection, in healthy and pathological conditions. Results High-quality 3D models were obtained. They can be utilized to assess the impact of the pathology, by volumetrically quantifying the extension of the affected areas. Indeed, based on the obtained reconstructions, an attempted classification for each patient in terms of the severity of the COVID-19 infection has been outlined. Conclusions Automatic algorithms allowed for a substantial reduction in segmentation time. However, a great effort was required for the manual identification of COVID-19 CT manifestations. The developed automated procedure succeeded in obtaining sufficiently accurate models of the airways and the lungs of both healthy patients and subjects with confirmed COVID-19, in a reasonable time.

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

“…An assessment of disease severity requires tools that can objectify the data to reduce the variability between patients due to qualitative evaluation. As to the “gravity assessment” of COVID-19 infection and evaluation of pulmonary parenchymal involvement, several scores have been proposed [ 62 , 63 ]. The main goal of these tools is to establish a well-defined strategy for evaluation of the airways and lungs of COVID-19 positive patients from Computed Tomography (CT) scans, including detected abnormalities [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ].…”

COVID-19 detection based on self-supervised transfer learning using chest X-ray images

“…Considering several patients screened due to COVID-19 pandemic, the use of deep learning for computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers [ 1 , 11 ]. Here, we proposed a new learning scheme called self-supervised transfer learning for COVID-19 detection using CXR images.…”

Evaluation of the Trachea and Bronchi in COVID-19 Patients Using the 3-dimensional Reconstruction Method

“…The disease was called COVID-19 by the World Health Organization. 1 Most studies have investigated lung parenchymal damage from COVID-19 disease. There is not enough information about the impacts of COVID-19 disease on the bronchi and trachea.…”