COVID-19 CT Scan Lung Segmentation: How We Do It

Abstract

The National Health Systems have been severely stressed out by the COVID-19 pandemic because 14% of patients require hospitalization and oxygen support, and 5% require admission to an Intensive Care Unit (ICU). Relationship between COVID-19 prognosis and the extent of alterations on chest CT obtained by both visual and software-based quantification that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one has been proven. While commercial applications for automatic medical image computing and visualization are expensive and limited in their spread, the open-source systems are characterized by not enough standardization and time-consuming troubles. We analyzed chest CT exams on 246 patients suspected of COVID-19 performed in the Emergency Department CT room. The lung parenchyma segmentation was obtained by a threshold-based method using the open-source 3D Slicer software and software tools called "Segment Editor" and "Segment Quantification." For the three main characteristics analyzed on lungs affected by COVID-19 pneumonia, a specifical densitometry value range was defined: from - 950 to - 700 HU for well-aerated parenchyma; from - 700 to - 250 HU for interstitial lung disease; from - 250 to 250 HU for parenchymal consolidation. For the well-aerated parenchyma and the interstitial alterations, the procedure was semi-automatic with low time consumption, whereas consolidations' analysis needed manual interventions by the operator. After the chest CT, 13% of the sample was admitted to intensive care, while 34% of them to the sub-intensive care. In patients moved to intensive care, the parenchyma analysis reported a higher crazy paving presentation. The quantitative analysis of the alterations affecting the lung parenchyma of patients with COVID-19 pneumonia can be performed by threshold method segmentation on 3D Slicer. The segmentation could have an important role in the quantification in different COVID-19 pneumonia presentations, allowing to help the clinician in the correct management of patients.

Keywords: COVID-19; Image processing, computer-assisted; Lung volume measurements; Pneumonia; Standards; Tomography, spiral computed.

Fig. 1

The segmentation protocol in COVID-19. The program used was 3D Slicer 4.10.2 (www.3dslicer.org). The time spent completing the flow chart was about 18 min (depending on hardware used), excluding the acquisition of the images

Fig. 2

All lung parenchyma + trachea and bronchi. The threshold-based segmentation was used to obtain the volume. Threshold =  − 1024; − 250 HU; smoothing method = closing, kernel size = 3 mm

Fig. 3

A Only the trachea and bronchi segmentation. The neighboring anatomy–guided segmentation was used to the volume estimation. Intensity tolerance = 60 voxels; neighborhood size = 1.0. B The COVID-19 GGO. The threshold-based segmentation was used to the volume estimation. Threshold =  − 700; − 250 HU; smoothing method = no smoothing. This threshold could isolate the GGO from the rest of the parenchyma. However, part of the pleura, inner part of the trachea, and peribronchovascular space are included in the volume. C COVID-19 consolidation. The threshold-based segmentation was conducted manually. Threshold =  − 250; + 250 HU; smoothing method = no smoothing. Diameter paint sphere = from 3 to 10 mm. D The well-aerated lung parenchyma. This volume was obtained indirectly by subtracting the GGO + trachea + bronchi from the total lung volume (Fig. 1)

Fig. 4

The main limits of this segmentation protocol. In detail: A many CT artifacts from breathing; B pleural and perivascular artifacts in GGO segmentation; C possible vascular inclusion during manual segmentation of consolidation; D the tertiary bronchi were not included in trachea and bronchi segmentation due to their exiguous dimension