Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

Abstract

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.

Keywords: 3D Visualization; COVID-19 lesion; Color-mapping; Computer-Aaided Ddetection (CAD); Corona-virus Ddisease (COVID-19); Segmentation.

Fig. 1

Flowchart of the proposed COVID-19 enhancement, segmentation, and visualization.

Fig. 2

Illustrative example of a (3-by-3) directional filter operation with a locally adaptive filter; (a) directional pattern; (b) (3-by-3) local adaptive filter.

Fig. 3

The proposed masking pneumonia regions comparison; (a) a CT-scan image; (b) a dilated structural image by using a ‘circle’ dilating filter; (c) a dilated structural image by using a ‘plus’ dilating filter; (d) a dilated structural image by using a ‘square’ dilating filter; (e) a dilated structural image by using a ‘circle’ dilating filter; (f) a visualized contrast metric; (g) a visualized contrast metric by using a ‘circle’ dilating filter; (h) a visualized contrast metric by using a ‘plus’ dilating filter; (i) a visualized contrast metric by using a ‘square’ dilating filter; (j) a visualized contrast metric by using a ‘circle’ dilating filter.

Algorithm 1

Masking algorithm: pseudo-code of multilevel thresholding.

Fig. 4

The proposed masking pneumonia regions comparison; (a) Original CT image; (b) one-dimensional histogram of the lung tissue region; (c) dilated image; (d) two-dimensional histogram; (e) one-dimensional histogram projected by a 2D histogram; (f) local minima numbers on histograms (the image dependent level number of thresholds).

Fig. 5

Results using Kapur threshold (a) input lung image, (b) 2-level thresholding, (c) 4-level, (d) 8-level of the proposed segmentation with their histograms. It is based on modified Kapur’s entropy computation (see Fig. 6).

Fig. 6

Illustrative example of the proposed segmentation lesion detection; (a) Enhanced CT scan; (b) Ground-Truth; (c) Proposed segmented mask; (d) Segmented mask using Medical image segmentation (MIS) ; (e) Segmented mask using GraphCut ; (f) Segmented mask using Watershed .

Fig. 7

Violin plots with median values of the proposed against Graph Cut, Watershed, and MIS segmentation methods using accuracy, sensitivity, F-Measure, precision, MCC, Dice, Jaccard, and specificity metrics.

Fig. 8

Visual comparison of COVID-19 infection segmentation results against GT.

Fig. 9

Visual assessment of COVID-19 severity level of the infection segmentation.

Fig. 10

3D visualization of the segmentation results.