Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis.

Young-Gon Kim,Mannudeep K Kalra,Min Kyu Kang,Soo Young Park,Dufan Wu,Yu Rim Lee,Byung Seok Kim,Woo Jin Chung,Quanzheng Li,Jung Gil Park,Hui Ren,Kyungsang Kim,Won Young Tak

doi:10.3390/diagnostics12010101

Abstract

Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients.

Highlights

The COVID-19 is a novel infectious disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which could lead to acute respiratory distress syndrome (ARDS) 1,2
RSNA pneumonia detection challenge dataset consists of 568 CXRs from tuberculosis chest dataset in department of health and human services (HHS) of Montgomery county and JSRT dataset consists of 257 CXRs from JSRT dataset in Japanese society of radiological technology (JSRT) in cooperation with the Japanese Radiological Society, were used to train segmentation models
We propose a novel and robust method to find a central point for segmentation of the whole region into four-regions such as right upper region (RUR), right lower region (RLR), low upper region (LUR), and left lower region (LLR)

Summary

Introduction

The COVID-19 is a novel infectious disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which could lead to acute respiratory distress syndrome (ARDS) 1,2. Despite its limitations and limited availability in several parts of both developed and developing world, most international and national organizations recommend RT-PCR assays for screening and initial diagnosis of COVID-19 infection. Use of imaging, computed tomography (CT) and chest radiography (CXR), for initial diagnosis of COVID19 pneumonia is extremely common in sites with high prevalence and/or limited availability of RT-PCR assays. There is consensus that imaging should be used judiciously, and most often, in patients with moderate to severe disease and those with complications and comorbidities. Both CT and CXR are used for establishing disease extent or severity of pulmonary opacities. Prior studies have reported on role of these imaging modalities for initial diagnosis and qualitative severity of COVID-19 pneumonia 2,4,5

Methods

Discussion

Conclusion