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Imaging plays an important role in assessing severity of COVID-19 pneumonia. The recent COVID-19 researches indicate that in many cases, disease progress propagates from the bottom of the lungs to the top. However, semantic interpretation of chest radiography (CXR) findings do not provide a quantitative description of radiographic opacities, and the existing AI-assisted CXR image analysis frameworks do not quantify the severity regionally. To address this issue, we propose a deep learning-based four-region lung segmentation method to assist accurate quantification of COVID-19 pneumonia. Specifically, a segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used to separate the upper and lower lungs. To improve the segmentation performance of COVID-19 images, ensemble strategy with five models is exploited. For each region, we evaluated the clinical relevance of the proposed method compared with the Radiographic Assessment of the Quality of Lung Edema (RALE). The proposed ensemble strategy showed dice score of 0.900, which outperforms the conventional methods. Mean intensities of segmented four regions indicate positive correlation to the extent and density scores of pulmonary opacities based on the RALE framework. Therefore, the proposed method can accurately segment four-regions of the lungs and quantify regional pulmonary opacities of COVID-19 pneumonia patients.