Deep learning segmentation model for automated detection of the opacity regions in the chest X-rays of the Covid-19 positive patients and the application for disease severity

Purpose

The pandemic of Covid-19 has caused tremendous losses to lives and economy in the entire world. The machine learning models have been applied to the radiological images of the Covid-19 positive patients for disease prediction and severity assessment. However, a segmentation model for detecting the opacity regions like haziness, ground-glass opacity and lung consolidation from the Covid-19 positive chest X-rays is still lacking.

Methods

The recently published collection of the radiological images for a rural population in United States had made the development of such a model a possibility, for the high quality images and consistent clinical measurements. We manually annotated 221 chest X-ray images with the lung fields and the opacity regions and trained a segmentation model for the opacity region using the Unet framework and the Resnet18 backbone. In addition, we applied the percentage of the opacity region over the area of the total lung fields for predicting the severity of patients.

Results

The model has a good performance regarding the overlap between the predicted and the manually labelled opacity regions. The performance is comparable for both the testing data set and the validation data set which comes from very diverse sources. However, careful manual examinations by experienced radiologists show mistakes in the predictions, which could be caused by the anatomical complexities. Nevertheless, the percentage of the opacity region can predict the severity of the patients well in regards to the ICU admissions and mortality.

Conclusion

In view of the above, our model is a successful first try in the development of a segmentation model for the opacity regions for the Covid-19 positive chest X-rays. However, additional work is needed before a robust model can be developed for the ultimate goal of the implementations in the clinical setting.

Model and supporting materials can be found in <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/haimingt/opacity_segmentation_covid_chest_X_ray">https://github.com/haimingt/opacity_segmentation_covid_chest_X_ray</ext-link>.