Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study

Abstract

Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity.

Figure 1:

Population-based difference atlas construction identifying statistically significant shape differences between the two populations. At first, the lung regions in the chest CTs are manually annotated and a template is chosen based on the median lung volume of a particular population. As part of the second step, all the CT volumes are registered to the chosen template and corresponding transformations are applied to the lung masks. Subsequently, a signed distance function is calculated on all the lung masks in the template space to represent shape information as a volume. Finally, a difference atlas (D_A) is generated using a generalized linear model-based T-test with 500 permutations.

Figure 2:

Flowchart of CovSafeNet. CovSafeNet consists of two parallel 3D CNNs (M₁, M₂). While M₁ encodes shape differences of the lung between mild and severe COVID-19 patients via a shape prior S_P (top row), M₂ encodes spatial information of automatically segmented lung infiltrates, I_P (bottom row). The decisions from M₁ and M₂ are fused at the decision fusion node (N_F) to obtain the final predictions predicting severity of COVID-19.

Figure 3:

Differences in lung volumes and mean intensities within in lung as seen on CTs between three different populations a) severe COVID-19 patients (patients who required a mechanical ventilator) from the training set D₁^train (green), b) mild COVID-19 patients (patients who did not require a mechanical ventilator) from the training set D₁^train (orange), c) healthy patients (no COVID-19) from D₅ (blue). The plot shows that as the severity of COVID-19 disease increases, the lung volume decreases. Similarly, it can be observed that mean lung intensities increases as the severity of COVID-19 disease increases.

Figure 4:

Ensemble difference atlas (D^E_A) between mild (patients who did not require a mechanical ventilator) and severe (patients who required a mechanical ventilator) COVID-19 patients. Majority of the lung shape differences between V⁻ and V⁺COVID-19 patients were found at the mediastinal surface of both lungs (Blue: No statistically significant shape difference between the populations. Red: Regions with statistically significant shape difference between the populations).

Figure 5:

Grad-CAM interpretability results with binary masks, S_P and I_P encoded into M₁ (1a-1h) and M₂ (2a-2h) respectively as auxiliary channels to the network. These maps show that these auxiliary channels can aid the network in setting an attention region helping the network to focus on these regions, while at the same time, providing the context of the whole lung region. Blue regions indicate areas contributing to predictions as mild COVID-19 disease while red regions indicate areas contributing to severe COVID-19 disease. The color bar gradient corresponds to the strength of the contribution.